--------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-re
> production\Pers-Protest.log
  log type:  text
 opened on:   9 Feb 2022, 16:22:15

.                 set scheme plotplain

.                 set more off 

.                 set matsize 1000
set matsize ignored.
    Matrix sizes are no longer limited by c(matsize) in modern Statas.  Matrix
    sizes are now limited by edition of Stata.  See limits for more details.

.                 global seed ="984353"

.         
.         **********************************************************************
.         *********  Gather and merge data sets; create using variables ********
.         **********************************************************************
.         /*
>                 * Clean covariate data sets *
>                 /*
>                         qui do clean-prio
>                         qui do clean-nmc
>                         qui do clean-nelda  /* downloaded 3.8.2018 */
>                 */
>                 * Coup data *
>                         set more off
>                         insheet using http://www.uky.edu/~clthyn2/coup_data/po
> well_thyne_coups_final.txt, clear  /* downloaded 3.8.2018 */
>                         rename ccode cowcode
>                         rename country pt_country
>                         * 1st coup in Yemen (ccode=680) 1968 is South Yemen bu
> t the second coup in August is actually in N/All-Yemen
>                         recode cow (679=678)
>                         recode cow (680=678) if year==1968 & month==8
>                         gen  p = "/"
>                         egen d = concat(day p month p year)
>                         gen date  = date(d, "DMY")
>                         gen coupA = coup==1
>                         gen coupS = coup==2
>                         drop p d
>                         local var = "coup coupA coupS"
>                         foreach v of local var {
>                                 bysort cow year: egen max`v'=max(`v')
>                                 replace `v' =max`v'
>                                 drop max`v'
>                         }
>                         egen tag= tag(cow year)
>                         keep if tag==1
>                         drop tag
>                         tsset cow year
>                         sort cow year
>                         saveold coups-merge,replace
>                         
>                 * EPR data *
>                         use epr-original, clear
>                         recode cow (260=255) (679=678) /* Yemen and West Germa
> ny are continuous regimes across unification */
>                         tsset cow year
>                         gen e = l.ethfrac
>                         replace ethfrac  = e 
>                         drop e
>                         tsset cow year
>                         gen excluded = l.lrexclpop 
>                         gen  loggdp = ln(gdpcapl)
>                         gen logoil = ln(1+oilpcl)
>                         tsset cow year
>                         gen gr= d.loggdp
>                         tssmooth ma  grow = gr, window(2 0 0)
>                         replace grow =gr if grow==. & gr~=.
>                         tsset cow year
>                         gen gr1 = l.gr
>                         replace gr = gr1
>                         drop gr1
>                         sort cow year
>                         saveold epr-merge,replace
>                         
>                 * Counter-weights data * downloaded on 1.25.2019 from:  https:
> //www.ethz.ch/content/dam/ethz/special-interest/gess/cis/international-relatio
> ns-dam/Data/Coup-Proofing%201970-2017.dta
>                         use counter-weights-original, clear
>                         gen cowcode = ccode
>                         recode cow (679=678) if year>1990
>                         sort cow year
>                         saveold  counter-merge,replace
>                         use SSFD-cy-jul2020,clear
>                         gen cowcode=ccode
>                         recode cow (679=678) if year>1990
>                         drop cw_* gwf_* milregimetype regime polity2 insample
>                         local var = "version pcount cbcount ha_cbcount affcoun
> t counterbalancing politicization ccount ptotal cbtotal afftotal pg intel poli
> ce troops militia military border  mean_p mean_cb mean_aff militiachange newmi
> litia pgchange newpg intelchange newintel policechange newpolice troopschange 
> newtroops milchange newmilitary borderchange newborder"
>                         foreach v of local var {
>                                 rename `v' debruin_`v'
>                         }
>                         sort cow year   
>                         tab debruin_cbcount
>                         recode debruin_cbcount (9 8 7 6 5 = 4)
>                         saveold debruin-merge,replace
>                 
>                 * NAVCO data * Version 2.1 (campaign-years from 1945-2013)  /*
>  downloaded 1.27.2020 from https://dataverse.harvard.edu/dataset.xhtml?persist
> entId=doi:10.7910/DVN/MHOXDV */
>                         use "NAVCO2-1_ForPublication.dta", clear 
>                         sum year  /* note that this ends in 2013 */
>                         gen start = cyear==0 & prim_meth==1
>                         gen start_regch = cyear==0 & prim_meth==1 & camp_goal=
> =0
>                         gen ongoing =  cyear>0 & prim_meth==1
>                         gen violent = 1 if prim_meth==0
>                         gen nonvio = 1 if prim_meth==1
>                         gen regch = 1 if camp_goal==0
>                         gen regchnv = camp_goal==0 & nonvio==1
>                         gen regchv = camp_goal==0 & violent==1
>                         gen duration = end_year-start_year+1
>                         gen cowcode = lccode
>                         recode cow (347=345) (364=365)        /* Russia/USSR g
> et same cowcode; Yugoslavia */
>                         recode cow (531=530) if year<1990     /* Eritrea coded
>  as Ethiopia prior to 1990 */
>                         local var = "duration start start_regch ongoing violen
> t nonvio regch regchnv regchv"
>                         foreach v of local var {
>                                 egen nav21_`v' = max(`v'), by(cow year)
>                         }
>                         egen tag = tag(cow year)
>                         rename location nav21_country
>                         keep if tag==1
>                         keep cowcode year nav21_*
>                         keep if year<=2010 & year>=1946
>                         sort cow year
>                         merge cow year using gwf-original
>                         tab _merge        /* note that GWF consider pre-1955 V
> ietnam as "not independent" */
>                         local var = "duration start start_regch ongoing violen
> t regchnv nonvio regchv"
>                         foreach v of local var {
>                                 tsset cowcode year
>                                 gen l1_nav21_`v' = l.nav21_`v' 
>                                 recode l1_nav21_`v' (.=0)
>                                 recode nav21_`v' (.=0)
>                         }               
>                         drop if _merge==1 
>                         drop _merge
>                         tsset cowcode year
>                         sort cowcode year
>                         order cowcode year nav21* l1_nav*
>                         save navco21-merge,replace
>                         
>                         
>                 * NAVCO 1.3 * Version 1.3 (campaigns from 1900-2019)  https://
> dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ON9XND on 9.2
> 1.2020
>                         import excel using "NAVCO13List.xlsx", clear firstrow 
>           
>                         gen duration = EYEAR- BYEAR+1
>                         list CAMPAIGN LOCATION BYEAR EYEAR FAILURE ONGOING   i
> f ONGOING==1  & EYEAR<2018
>                         drop ONGOING
>                         expand duration
>                         sort NAVCOID
>                         gen n=_n
>                         egen min=min(n),by(NAVCOID)
>                         gen year = BYEAR if n==min
>                         bysort NAVCOID: replace year = year[_n-1] + 1 if n>min
>  & year==.
>                         gen country  =LOCATION
>                         replace country="Congo Brazzaville" if country=="Congo
> -Brazzaville (ROC)"
>                         replace country="Congo, Dem. Rep." if country=="Democr
> atic Republic of Congo"
>                         replace country="Germany East" if country=="East Germa
> ny"
>                         replace country="Yemen, Republic of" if country=="Repu
> blic of Yemen"
>                         replace country="Congo Brazzaville" if country=="Repub
> lic of the Congo"
>                         replace country="Yemen Arab Rep" if country=="Yemen Ar
> ab Republic"
>                         replace country="Yemen, (PDR)" if country=="Yemen Peop
> le's Republic"
>                         replace country="Congo/Zaire" if country=="Zaire/DRC"
>                         gen cowcode=.
>                         replace cowcode =626 if country=="South Sudan"
>                         qui do cowcodes
>                         tab LOCATION if cowcode==. & year>=1946
>                         drop if year<1946 | cowcode==. | year==.
>                         gen violent = VIOL==1
>                         gen violent_regch = VIOL==1 & REGCHANGE==1
>                         gen nonviolent = NONVIOL==1
>                         gen nonviolent_regch = NONVIOL==1 & REGCHANGE==1
>                         gen start = nonviolent==1 & year==BYEAR
>                         gen start_regch = nonviolent==1 & year==BYEAR & REGCHA
> NGE==1
>                         gen ongoing = nonviolent==1 & year>BYEAR & year<=EYEAR
>   
>                         gen p="-"
>                         egen idyear = concat(cowcode p year)
>                         local var = "duration start start_regch ongoing violen
> t violent_regch nonviolent nonviolent_regch"
>                         foreach v of local var {
>                                 egen nav13_`v' = max(`v'),by(idyear)
>                         }
>                         egen tag=tag(idyear)
>                         gen nav13_country = country
>                         sum cowcode year nav13_*
>                         keep if tag==1
>                         keep cowcode year nav13_*
>                         keep if year<=2010 & year>=1946
>                         tsset cowcode year
>                         sort cowcode year
>                         merge cow year using gwf-original
>                         tab _merge
>                         local var = "duration start start_regch ongoing violen
> t violent_regch nonviolent nonviolent_regch"
>                         foreach v of local var {
>                                 tsset cowcode year
>                                 gen l1_nav13_`v' = l.nav13_`v' 
>                                 recode l1_nav13_`v' (.=0)
>                                 recode nav13_`v' (.=0)
>                         }                       
>                         drop if _merge==1 
>                         drop _merge
>                         tsset cowcode year
>                         sort cowcode year
>                         order cowcode year nav13* l1_nav*
>                         save navco13-merge,replace
>                         
>                         
>                 * MEC data *  https://github.com/ulfelder/nonviolent-uprisings
> -replication/raw/8646b4410983dd4877c32b7bbdeac91d40d3413d/data/nvc.transformed
> .csv
>                         use nvc-original, clear
>                         keep year country sftgcode yrborn yrdied nvcstart nvc*
>  yth* pol* wditrade wdipopurbmi
>                         gen cowcode =.
>                         qui do cowcodes
>                         replace cowcode = 490 if country=="Congo-Kinshasa"
>                         replace cowcode = 265 if country=="East Germany"
>                         replace cowcode = 345 if country=="Federal Republic of
>  Yugoslavia"  /* Serbia 1992-2002 */
>                         replace cowcode = 816 if country=="North Vietnam"
>                         sort cow year
>                         gen repeat=1 if cow==cow[_n-1] & year==year[_n-1]
>                         drop if repeat==1
>                         xtset cow year
>                         gen lag_nvcongoing = l.nvcongoing
>                         sort cow year
>                         save mec-merge,replace
>                          
>                 * RENAVCO region data *
>                         use renavco, clear
>                         gen cowcode =.
>                         gen country = statenme
>                         qui do cowcodes
>                         sum cowcode  ccode
>                         replace cowcode = 490 if country=="Democratic Republic
>  of the Congo"
>                         replace cowcode = 265 if country=="German Democratic R
> epublic"
>                         replace cowcode = 817 if country=="Republic of Vietnam
> "
>                         replace cowcode = 678 if country=="Yemen Arab Republic
> "
>                         replace cowcode = 680 if country=="Yemen People's Repu
> blic"
>                         tab country if cowcode==.  /* none are GWF autocracies
> , 1946-2010 */
>                         drop if cowcode==.
>                         sort cow year
>                         gen repeat = cow==cow[_n-1] & year==year[_n-1]
>                         list country year if repeat==1
>                         drop if repeat==1
>                         drop repeat
>                         xtset cow year
>                         gen lnregion = l.renavco_nvc_onset_lregcnt
>                         replace lnregion = renavco_nvc_onset_lregcnt if year==
> 1946
>                         tab lnregion
>                         gen lnregion2  = lnregion
>                         replace lnregion2= 1.61 if lnregion>1.6 & lnregion~=.
>                         keep cowcode year country lnregion renavco_nvc_onset_l
> regcnt
>                         sort cow year
>                         save renavco-region-merge,replace
>  
>                 * Fariss data *   /* downloaded 3.8.2018 */
>                         use fariss-original,clear
>                         recode cow (679=678) if year>1990
>                         local var="ciri disap kill polpris tort amnesty state 
> hathaway itt genocide rummel massive_repression executions killing additive"
>                         foreach v of local var {
>                                 replace `v' = "." if `v'=="NA"
>                                 destring `v', replace
>                         }
>                         sort cow year
>                         xtset cow year
>                         gen  r1 = latentmean*-1   /* flip scale */
>                         tssmooth ma lag_repress=r1, window(3 0 0)
>                         tssmooth ma lag2_repress=r1, window(2 0 0)
>                         gen l1r = l1.r1
>                         gen l2r = l2.r1
>                         gen l3r = l3.r1
>                         replace l3r=l2r if l3r==.
>                         replace l3r=l1r if l3r==.
>                         replace l2r=l1r if l2r==.
>                         replace lag_repress =lag2_repress if lag_repress==.
>                         replace lag_repress=l1r if lag_repress==.
>                         xtset cow year
>                         gen lag1repress=l1.r1
>                         gen lag2repress=l2.r1
>                         gen lag3repress=l3.r1
>                         drop r1 lag2_repress
>                         saveold fariss-merge,replace
>                                 
>                 * VDem data * /* downloaded 6.26.2020 from https://www.v-dem.n
> et/en/data/data-version-10/ */
>                         cd "$dir"
>                         use V-Dem-CY-Full+Others-v10,clear
>                         keep country_name year COWcode v2x_polyarchy v2x_libde
> m v2x_partipdem v2x_freexp_altinf v2x_frassoc_thick ///
>                                 v2x_clpol v2x_jucon v2juhcind v2cltort v2clkil
> l v2x_corr e_migdppc e_migdppcln v2caconmob v2cademmob v2caautmob
>                         rename country_name vdem_country
>                         rename COWcode cowcode
>                         recode cow (679=678) if year>1990
>                         tab vdem_country if cow==.
>                         drop if cow==.
>                         tab vdem_country if cowcode==99999   /* cases in our s
> ample */
>                         drop if cowcode==99999
>                         tsset cow year
>                         sort cowcode year
>                         save "vdem-merge.dta", replace
>                         use V-Dem-CY-Core-v8_merge,clear
>                         gen cowcode =ccode
>                         recode cow (679=678) if year>1990
>                         sort cowcode year
>                         merge cow year using vdem-merge
>                         tab _merge
>                         rename _merge v2merge
>                         sum year cow
>                         xtset cow year
>                         alpha v2clkill v2cltort, item std gen(vkill)
>                         replace vkill = vkill*-1
>                         tssmooth ma lag_vkill= vkill,window(3 0 0)
>                         local var = "v2caconmob v2cademmob v2caautmob v2x_poly
> archy v2x_partipdem v2x_freexp_altinf v2x_frassoc_thick v2x_clpol v2x_jucon v2
> juhcind e_v2x_neopat"
>                         foreach v of local var {
>                                 gen l1`v'=l1.`v'
>                                 gen l2`v'=l2.`v'
>                                 gen l3`v'=l3.`v'
>                                 gen l4`v'=l4.`v'
>                         }
>                         sort cow year
>                         save vdem-merge, replace
> */
.                 * Personalism data *
.                         use gwf-original,clear                  

.                         * Generate binary variables *
.                         gen milmerit_persA = milmerit_pers

.                         recode milmerit_persA (2=1) (1=1)
(1943 changes made to milmerit_persA)

.                         gen milmerit_persB = milmerit_pers

.                         recode milmerit_persB (2=1) (1=0)  
(3502 changes made to milmerit_persB)

.                         tsset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                         gen newparty =support==1 & l.support==0

.                         tsset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                         replace newparty=1 if l.newparty==1 & l.gwf_leaderid==
> gwf_leaderid & year==year[_n-1]+1
(659 real changes made)

.                         gen createparty =militparty_new==1 | (newparty==1  & p
> artyhistory_post==1) 

.                         gen partyorigin = ldr_group_domparty==1 | ldr_group_pr
> iordem==1

.                         gen offpersmil = officepers*ldr_group_military

.                         gen offpersparty = officepers*partyorigin 

.                         tab offpers*

           |     offpersparty
offpersmil |         0          1 |     Total
-----------+----------------------+----------
         0 |     2,954        797 |     3,751 
         1 |       840          0 |       840 
-----------+----------------------+----------
     Total |     3,794        797 |     4,591 

.                         
.                         * Label variables *
.                         global pvars1 = "partyexcom_pers partyrbr offpersparty
>  createparty"

.                         global pvars2 = "milnotrial milmerit_persB paramil_per
> s sectyapp_pers offpersmil"

.                         label var offpersparty "Appointments to high office, p
> arty"

.                         label var offpersmil "Appointments to high office, mil
> itary"

.                         label var createparty "Create new party"

.                         label var partyexcom_pers "Party exec committee"

.                         label var partyrbr "Rubber stamp party"

.                         label var milmerit_persB "Military promotions"

.                         label var milnotrial "Military purge"

.                         label var sectyapp_pers "Security apparatus"

.                         label var paramil_pers "Paramilitary"

.                         
.                         * Personalism variables *
.                         set seed 2453456

.                         irt (2pl partyexcom_pers partyrbr officepers createpar
> ty milnotrial milmerit_persB paramil_pers sectyapp_pers) 

Fitting fixed-effects model:

Iteration 0:   log likelihood = -22969.605  
Iteration 1:   log likelihood =  -22931.47  
Iteration 2:   log likelihood = -22931.455  
Iteration 3:   log likelihood = -22931.455  

Fitting full model:

Iteration 0:   log likelihood = -21492.543  
Iteration 1:   log likelihood = -20095.733  
Iteration 2:   log likelihood = -20026.085  
Iteration 3:   log likelihood = -19907.278  
Iteration 4:   log likelihood = -19904.262  
Iteration 5:   log likelihood = -19904.256  
Iteration 6:   log likelihood = -19904.256  

Two-parameter logistic model                             Number of obs = 4,591
Log likelihood = -19904.256
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
partyexcom~s |
     Discrim |   2.131928   .1134983    18.78   0.000     1.909476    2.354381
        Diff |   .6085253   .0280052    21.73   0.000      .553636    .6634145
-------------+----------------------------------------------------------------
partyrbrstmp |
     Discrim |   2.004324   .1048153    19.12   0.000      1.79889    2.209759
        Diff |   .6785336   .0298494    22.73   0.000     .6200299    .7370373
-------------+----------------------------------------------------------------
officepers   |
     Discrim |   2.890977   .1530346    18.89   0.000     2.591034    3.190919
        Diff |  -.4202195   .0232182   -18.10   0.000    -.4657263   -.3747127
-------------+----------------------------------------------------------------
createparty  |
     Discrim |   1.283182   .0690587    18.58   0.000      1.14783    1.418535
        Diff |   1.644761   .0679592    24.20   0.000     1.511564    1.777959
-------------+----------------------------------------------------------------
milnotrial   |
     Discrim |   1.558655    .070848    22.00   0.000     1.419795    1.697514
        Diff |   .5093505   .0305189    16.69   0.000     .4495346    .5691664
-------------+----------------------------------------------------------------
milmerit_p~B |
     Discrim |   1.366218   .0621683    21.98   0.000     1.244371    1.488066
        Diff |   .3094893   .0305383    10.13   0.000     .2496355    .3693432
-------------+----------------------------------------------------------------
paramil_pers |
     Discrim |   1.111254   .0554924    20.03   0.000     1.002491    1.220017
        Diff |   .6777748   .0411037    16.49   0.000     .5972129    .7583367
-------------+----------------------------------------------------------------
sectyapp_p~s |
     Discrim |   1.760407   .0783432    22.47   0.000     1.606858    1.913957
        Diff |   -.332487   .0270983   -12.27   0.000    -.3855987   -.2793753
------------------------------------------------------------------------------

.                         estat report partyexcom_pers partyrbr officepers creat
> eparty milnotrial milmerit_persB paramil_pers sectyapp_pers, byparm sort(b)

Two-parameter logistic model                             Number of obs = 4,591
Log likelihood = -19904.256
-------------------------------------------------------------------------------
              | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
Discrim       |
   officepers |   2.890977   .1530346    18.89   0.000     2.591034    3.190919
sectyapp_pers |   1.760407   .0783432    22.47   0.000     1.606858    1.913957
milmerit_pe~B |   1.366218   .0621683    21.98   0.000     1.244371    1.488066
   milnotrial |   1.558655    .070848    22.00   0.000     1.419795    1.697514
partyexcom_~s |   2.131928   .1134983    18.78   0.000     1.909476    2.354381
 paramil_pers |   1.111254   .0554924    20.03   0.000     1.002491    1.220017
 partyrbrstmp |   2.004324   .1048153    19.12   0.000      1.79889    2.209759
  createparty |   1.283182   .0690587    18.58   0.000      1.14783    1.418535
--------------+----------------------------------------------------------------
Diff          |
   officepers |  -.4202195   .0232182   -18.10   0.000    -.4657263   -.3747127
sectyapp_pers |   -.332487   .0270983   -12.27   0.000    -.3855987   -.2793753
milmerit_pe~B |   .3094893   .0305383    10.13   0.000     .2496355    .3693432
   milnotrial |   .5093505   .0305189    16.69   0.000     .4495346    .5691664
partyexcom_~s |   .6085253   .0280052    21.73   0.000      .553636    .6634145
 paramil_pers |   .6777748   .0411037    16.49   0.000     .5972129    .7583367
 partyrbrstmp |   .6785336   .0298494    22.73   0.000     .6200299    .7370373
  createparty |   1.644761   .0679592    24.20   0.000     1.511564    1.777959
-------------------------------------------------------------------------------

.                         predict pers_2pl, latent se(pers_se_2pl)
(option ebmeans assumed)
(using 7 quadrature points)

.                         qui sum pers_2pl

.                         gen xpers = (pers_2pl+abs(r(min))) / (r(max) - r(min))

.                         hist xpers, bin(50)
(bin=50, start=0, width=.02)

.                         irt (2pl milnotrial paramil_pers sectyapp_pers offpers
> mil) (grm milmerit_pers)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -16747.398  
Iteration 1:   log likelihood = -16219.464  
Iteration 2:   log likelihood = -16199.912  
Iteration 3:   log likelihood = -16199.859  
Iteration 4:   log likelihood = -16199.859  

Fitting full model:

Iteration 0:   log likelihood = -15132.577  
Iteration 1:   log likelihood = -14556.002  
Iteration 2:   log likelihood = -14533.428  
Iteration 3:   log likelihood = -14531.524  
Iteration 4:   log likelihood =  -14531.51  
Iteration 5:   log likelihood =  -14531.51  

Hybrid IRT model                                         Number of obs = 4,591
Log likelihood = -14531.51
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------------------------------------------------------------------
2pl         
------------------------------------------------------------------------------
milnotrial   |
     Discrim |   2.838022   .1728143    16.42   0.000     2.499312    3.176732
        Diff |   .4122978   .0230835    17.86   0.000      .367055    .4575406
-------------+----------------------------------------------------------------
paramil_pers |
     Discrim |   1.192022   .0611608    19.49   0.000     1.072149    1.311895
        Diff |   .6494227    .039031    16.64   0.000     .5729233    .7259221
-------------+----------------------------------------------------------------
sectyapp_p~s |
     Discrim |   1.502581   .0728697    20.62   0.000     1.359759    1.645403
        Diff |   -.360889   .0299435   -12.05   0.000    -.4195772   -.3022008
-------------+----------------------------------------------------------------
offpersmil   |
     Discrim |   1.309045   .0724623    18.07   0.000     1.167022    1.451069
        Diff |   1.484703   .0625755    23.73   0.000     1.362057    1.607348
------------------------------------------------------------------------------
grm         
------------------------------------------------------------------------------
milmerit_p~s |
     Discrim |   2.257298   .1015904    22.22   0.000     2.058185    2.456412
        Diff |
        >=1  |  -.8986512   .0309331                      -.959279   -.8380233
         =2  |   .2577678   .0237847                      .2111506     .304385
------------------------------------------------------------------------------

.                         predict irtpers2, latent  
(option ebmeans assumed)
(using 7 quadrature points)

.                         qui sum irt

.                         replace irt = (irt+abs(r(min))) / (r(max) - r(min))
(4,591 real changes made)

.                         alpha $pvars2, std gen(ypers2) item

Test scale = mean(standardized items)

                                                            Average
                             Item-test     Item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     correlation     alpha
-------------+-----------------------------------------------------------------
milnotrial   | 4591    +       0.7225        0.5177          0.2550      0.5779
milmerit_p~B | 4591    +       0.6927        0.4743          0.2713      0.5983
paramil_pers | 4591    +       0.6301        0.3871          0.3056      0.6378
sectyapp_p~s | 4591    +       0.6726        0.4457          0.2824      0.6115
offpersmil   | 4591    +       0.5702        0.3081          0.3385      0.6718
-------------+-----------------------------------------------------------------
Test scale   |                                               0.2906      0.6719
-------------------------------------------------------------------------------

.                         qui sum ypers2

.                         replace ypers2 = (ypers2+abs(r(min))) / (r(max) - r(mi
> n))
(4,591 real changes made)

.                         alpha $pvars1, std gen(xpers1) item

Test scale = mean(standardized items)

                                                            Average
                             Item-test     Item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     correlation     alpha
-------------+-----------------------------------------------------------------
partyexcom~s | 4591    +       0.7911        0.5536          0.1081      0.2666
partyrbrstmp | 4591    +       0.8173        0.6005          0.0854      0.2189
offpersparty | 4591    +       0.4828        0.1115          0.3754      0.6432
createparty  | 4591    +       0.5095        0.1437          0.3523      0.6200
-------------+-----------------------------------------------------------------
Test scale   |                                               0.2303      0.5448
-------------------------------------------------------------------------------

.                         qui sum xpers1

.                         replace xpers1 = (xpers1+abs(r(min))) / (r(max) - r(mi
> n))
(4,591 real changes made)

.                         alpha partyexcom_pers partyrbr officepers  createparty
>  pleb, std   item gen(ypers1)

Test scale = mean(standardized items)

                                                            Average
                             Item-test     Item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     correlation     alpha
-------------+-----------------------------------------------------------------
partyexcom~s | 4591    +       0.7499        0.5588          0.2400      0.5581
partyrbrstmp | 4591    +       0.7728        0.5940          0.2274      0.5408
officepers   | 4591    +       0.6452        0.4077          0.2973      0.6286
createparty  | 4591    +       0.5624        0.2982          0.3427      0.6759
plebiscite   | 4591    +       0.5577        0.2921          0.3453      0.6784
-------------+-----------------------------------------------------------------
Test scale   |                                               0.2906      0.6719
-------------------------------------------------------------------------------

.                         qui sum ypers1

.                         replace ypers1 = (ypers1+abs(r(min))) / (r(max) - r(mi
> n))
(4,591 real changes made)

.                 
.                         * Linear link for paramil_pers sectyapp_pers to allow 
> their errors to be correlated *
.                         gsem (PER->paramil_pers sectyapp_pers,reg var(PER@1)vc
> e(cluster gwf_leaderid) ///
>                                 cov(e.sectyapp_pers*e.paramil_pers)) ///
>                                 (PER->milnotrial offpersmil,logit) (PER-> milm
> erit_pers,ologit)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -16498.765  
Iteration 1:   log likelihood = -16149.377  
Iteration 2:   log likelihood = -16001.636  
Iteration 3:   log likelihood = -15996.425  
Iteration 4:   log likelihood = -15996.419  
Iteration 5:   log likelihood = -15996.419  

Refining starting values:

Grid node 0:   log likelihood =  -15818.66

Fitting full model:

Iteration 0:   log pseudolikelihood =  -15818.66  (not concave)
Iteration 1:   log pseudolikelihood = -14969.552  
Iteration 2:   log pseudolikelihood =  -14760.04  
Iteration 3:   log pseudolikelihood = -14628.133  
Iteration 4:   log pseudolikelihood = -14603.085  
Iteration 5:   log pseudolikelihood = -14602.871  
Iteration 6:   log pseudolikelihood = -14602.871  

Generalized structural equation model                    Number of obs = 4,591

Response: paramil_pers 
Family:   Gaussian     
Link:     Identity     

Response: sectyapp_pers
Family:   Gaussian     
Link:     Identity     

Response: milnotrial   
Family:   Bernoulli    
Link:     Logit        

Response: offpersmil   
Family:   Bernoulli    
Link:     Logit        

Response: milmerit_pers
Family:   Ordinal      
Link:     Logit        

Log pseudolikelihood = -14602.871

 ( 1)  [/]var(PER) = 1
                          (Std. err. adjusted for 505 clusters in gwf_leaderid)
-------------------------------------------------------------------------------
              |               Robust
              | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
paramil_pers  |
          PER |   .1752281   .0278931     6.28   0.000     .1205587    .2298975
        _cons |   .3539499   .0287277    12.32   0.000     .2976446    .4102551
--------------+----------------------------------------------------------------
sectyapp_pers |
          PER |   .2268164   .0218479    10.38   0.000     .1839953    .2696374
        _cons |   .5964438   .0261601    22.80   0.000     .5451709    .6477166
--------------+----------------------------------------------------------------
milnotrial    |
          PER |   3.206082   .8342827     3.84   0.000     1.570918    4.841246
        _cons |  -1.298837   .3336466    -3.89   0.000    -1.952772   -.6449015
--------------+----------------------------------------------------------------
offpersmil    |
          PER |   1.359648   .2592429     5.24   0.000     .8515417    1.867755
        _cons |  -1.975811   .2171572    -9.10   0.000    -2.401431    -1.55019
--------------+----------------------------------------------------------------
milmerit_pers |
          PER |   2.443487   .4582359     5.33   0.000     1.545361    3.341613
--------------+----------------------------------------------------------------
/milmerit_p~s |
         cut1 |  -2.151347   .3290414                     -2.796257   -1.506438
         cut2 |    .620285    .226959                      .1754536    1.065116
--------------+----------------------------------------------------------------
      var(PER)|          1  (constrained)
--------------+----------------------------------------------------------------
var(e.param~s)|   .1979032   .0107217                      .1779662    .2200735
var(e.secty~s)|   .1893086   .0101776                      .1703759    .2103451
--------------+----------------------------------------------------------------
cov(e.param~s,|
e.sectyapp_~s)|   .0636824   .0113576     5.61   0.000     .0414219     .085943
-------------------------------------------------------------------------------

.                         predict xpers2, latent ebmeans
(using 7 quadrature points)

.                         qui sum xpers2

.                         replace xpers2 = (xpers2+abs(r(min))) / (r(max) - r(mi
> n))
(4,591 real changes made)

.                         estat ic                                

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |          N   ll(null)  ll(model)      df        AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591          .  -14602.87      14   29233.74   29323.79
-----------------------------------------------------------------------------
Note: BIC uses N = number of observations. See [R] BIC note.

.                          * GSEM w/out covariances *
.                         qui gsem (PER->paramil_pers sectyapp_pers,reg var(PER@
> 1)vce(cluster gwf_leaderid)) ///
>                                 (PER->milnotrial offpersmil,logit) (PER-> milm
> erit_pers,ologit)

.                         estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |          N   ll(null)  ll(model)      df        AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591          .  -14824.75      13   29675.49   29759.11
-----------------------------------------------------------------------------
Note: BIC uses N = number of observations. See [R] BIC note.

.                         
.                         * Additional covariances with sectyapp_pers *  More co
> mplex covariance structures do not improve fit 
.                                         * First look at the full Gaussian mode
> l with 1 covariance specified, for comparison *
.                         qui gsem (PER-> sectyapp_pers paramil_pers milmerit_pe
> rs milnotrial offpersmil,reg var(PER@1)vce(cluster gwf_leaderid) ///
>                                 cov(e.sectyapp_pers*e.paramil_pers)) 

.                         estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |          N   ll(null)  ll(model)      df        AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591          .  -15217.26      12   30458.53   30535.71
-----------------------------------------------------------------------------
Note: BIC uses N = number of observations. See [R] BIC note.

.                         local var "milmerit_pers milnotrial offpersmil"

.                         foreach v of local var {
  2.                                 qui gsem (PER-> sectyapp_pers paramil_pers 
> milmerit_pers milnotrial offpersmil,reg var(PER@1)vce(cluster gwf_leaderid) //
> /
>                                         cov(e.sectyapp_pers*e.paramil_pers)cov
> (e.sectyapp_pers*e.`v')) 
  3.                                 estat ic
  4.                         }

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |          N   ll(null)  ll(model)      df        AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591          .  -15214.49      14   30456.99   30547.03
-----------------------------------------------------------------------------
Note: BIC uses N = number of observations. See [R] BIC note.

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |          N   ll(null)  ll(model)      df        AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591          .  -15214.64      14   30457.27   30547.32
-----------------------------------------------------------------------------
Note: BIC uses N = number of observations. See [R] BIC note.

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |          N   ll(null)  ll(model)      df        AIC        BIC
-------------+---------------------------------------------------------------
           . |      4,591          .  -15217.25      14    30462.5   30552.55
-----------------------------------------------------------------------------
Note: BIC uses N = number of observations. See [R] BIC note.

.                                 
.                         sum xpers1 xpers2 xpers 

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      xpers1 |      4,591     .231475    .2687455          0          1
      xpers2 |      4,591    .4670486    .3038622          0          1
       xpers |      4,591    .4199273    .2770179          0          1

.                         spearman xpers1 xpers2 xpers    
(obs=4591)

             |   xpers1   xpers2    xpers
-------------+---------------------------
      xpers1 |   1.0000 
      xpers2 |   0.3214   1.0000 
       xpers |   0.7476   0.7563   1.0000 

.                         twoway lpolyci xpers1 xpers2,xtit(Security personalism
> )ytit(Party personalism)legend(off)

.                         
.                         twoway (scatter irtpers xpers2,xtit("Generalized SEM")
> ytit("Hybrid 2PL + GRM")) (lpoly irtpers xpers2,lcol(blue)lpat(solid)) ///
>                                 (lfit   irtpers xpers2,lpat(solid)lcol(red)leg
> end(lab(2 "nonlinear fit") lab(3 "linear fit") order(2 3) ///
>                                 pos(5)ring(0))text(.7 .3 "{&rho}=0.9915"))

.                         graph export "$dir\sfp-logit-vs-general.pdf", as(pdf) 
>   replace
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\sfp-logit-vs-general.pdf saved as PDF format

. 
.                          
.                         * IRT plots *
.                         qui irt 2pl partyexcom_pers partyrbr officepers create
> party milnotrial milmerit_persB paramil_pers sectyapp_pers

.                         irtgraph iif    (sectyapp_pers,lcolor(blue)) (milmerit
> _persB,lcolor(red)) (milnotrial,lcolor(green)) ///
>                         (paramil_pers,lcolor(cyan)),legend(col(2) pos(6)) titl
> e(Security & military items) saving(t2,replace) ///
>                         ylab(,glcolor(gs15)) xtitle("Personalism ({&theta})")
(file t2.gph not found)
file t2.gph saved

.                         irtgraph iif  (officepers,lcolor(blue)) (partyexcom_pe
> rs,lcolor(red)) (partyrbr,lcolor(green)) ///
>                         (createparty,lcolor(cyan)),legend(col(2) pos(6))  titl
> e(Party & personnel items) saving(t1,replace) ///
>                         ylab(,glcolor(gs15)) xtitle("Personalism ({&theta})")
(file t1.gph not found)
file t1.gph saved

.                         gr combine t1.gph t2.gph, col(2)   ysize(5.5) xsize(9)
>   ycommon

.                         erase t1.gph

.                         erase t2.gph

. 
.                         * Variance decomposition *
.                         qui xtset gwf_caseid year

.                         foreach v in xpers xpers1 xpers2 ypers2 irtpers {
  2.                                 qui xtsum  `v'  
  3.                                 qui scalar sdb = r(sd_b)
  4.                                 qui scalar sdw = r(sd_w)
  5.                                 qui scalar vart= sdb + sdw
  6.                                 qui scalar varr = sdw / vart
  7.                                 scalar list sdw
  8.                                 scalar list varr
  9.                         }
       sdw =  .14805139
      varr =  .37596609
       sdw =  .15618016
      varr =  .41825498
       sdw =  .14951971
      varr =  .35179033
       sdw =  .13834791
      varr =  .33162364
       sdw =   .1418389
      varr =  .34654829

.                         sort cow year

.                         save temp-pers,replace
file temp-pers.dta saved

.                         
.                 * Merge data sets *
.                         use temp-pers,clear

.                         merge cow year using fariss-merge
(you are using old merge syntax; see [D] merge for new syntax)
(variable cowcode was int, now float to accommodate using data's values)

.                         tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        122        1.30        1.30
          2 |      4,799       51.11       52.41
          3 |      4,469       47.59      100.00
------------+-----------------------------------
      Total |      9,390      100.00

.                         drop if _merge==2
(4,799 observations deleted)

.                         tab year if _merge==1  /* note that 1946-1948 are not 
> in the Fariss data */

       year |      Freq.     Percent        Cum.
------------+-----------------------------------
       1946 |         32       26.23       26.23
       1947 |         32       26.23       52.46
       1948 |         32       26.23       78.69
       1949 |          1        0.82       79.51
       1950 |          2        1.64       81.15
       1951 |          2        1.64       82.79
       1952 |          2        1.64       84.43
       1953 |          2        1.64       86.07
       1954 |          1        0.82       86.89
       1955 |          1        0.82       87.70
       1956 |          1        0.82       88.52
       1957 |          1        0.82       89.34
       1958 |          1        0.82       90.16
       1959 |          1        0.82       90.98
       1960 |          1        0.82       91.80
       1961 |          1        0.82       92.62
       1962 |          1        0.82       93.44
       1963 |          1        0.82       94.26
       1964 |          1        0.82       95.08
       1965 |          1        0.82       95.90
       1966 |          1        0.82       96.72
       1967 |          1        0.82       97.54
       1968 |          1        0.82       98.36
       1969 |          1        0.82       99.18
       1970 |          1        0.82      100.00
------------+-----------------------------------
      Total |        122      100.00

.                         rename _merge merge1

.                         sort cow year

.                         save temp,replace
file temp.dta saved

.                     merge cow year using navco13-merge
(you are using old merge syntax; see [D] merge for new syntax)
(variable year was int, now float to accommodate using data's values)

.                         tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          3 |      4,591      100.00      100.00
------------+-----------------------------------
      Total |      4,591      100.00

.                         drop if _merge==2
(0 observations deleted)

.                         rename _merge merge2

.                         sort cow year

.                         save temp,replace       
file temp.dta saved

.                         merge cow year using navco21-merge
(you are using old merge syntax; see [D] merge for new syntax)
(variable year was float, now double to accommodate using data's values)

.                         tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          3 |      4,591      100.00      100.00
------------+-----------------------------------
      Total |      4,591      100.00

.                         drop if _merge==2
(0 observations deleted)

.                         rename _merge merge3

.                         sort cow year

.                         save temp,replace       
file temp.dta saved

.                         merge cow year using epr-merge
(you are using old merge syntax; see [D] merge for new syntax)
(variable country was str15, now str32 to accommodate using data's values)

.                         tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         76        0.95        0.95
          2 |      3,393       42.50       43.45
          3 |      4,515       56.55      100.00
------------+-----------------------------------
      Total |      7,984      100.00

.                         tab gwf_country if _merge==1  /* note that Singapore a
> nd Oman are not in EPR data */

          Country name |      Freq.     Percent        Cum.
-----------------------+-----------------------------------
          Germany East |          5        6.58        6.58
                  Oman |         25       32.89       39.47
             Singapore |         45       59.21       98.68
           South Yemen |          1        1.32      100.00
-----------------------+-----------------------------------
                 Total |         76      100.00

.                         drop if _merge==2
(3,393 observations deleted)

.                         rename _merge merge4

.                         sort cow year

.                         save temp,replace
file temp.dta saved

.                         merge cow year using coups-merge
(you are using old merge syntax; see [D] merge for new syntax)

.                         tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      4,320       91.14       91.14
          2 |        149        3.14       94.28
          3 |        271        5.72      100.00
------------+-----------------------------------
      Total |      4,740      100.00

.                         tab year if _merge==2  /* note that coup data begin in
>  1950 */

       year |      Freq.     Percent        Cum.
------------+-----------------------------------
       1950 |          1        0.67        0.67
       1951 |          1        0.67        1.34
       1952 |          2        1.34        2.68
       1954 |          2        1.34        4.03
       1955 |          2        1.34        5.37
       1956 |          1        0.67        6.04
       1957 |          1        0.67        6.71
       1958 |          2        1.34        8.05
       1959 |          2        1.34        9.40
       1960 |          2        1.34       10.74
       1961 |          7        4.70       15.44
       1962 |          5        3.36       18.79
       1963 |          5        3.36       22.15
       1964 |          2        1.34       23.49
       1965 |          1        0.67       24.16
       1966 |          4        2.68       26.85
       1967 |          2        1.34       28.19
       1968 |          2        1.34       29.53
       1969 |          3        2.01       31.54
       1971 |          1        0.67       32.21
       1972 |          4        2.68       34.90
       1973 |          3        2.01       36.91
       1974 |          1        0.67       37.58
       1975 |          4        2.68       40.27
       1976 |          3        2.01       42.28
       1977 |          1        0.67       42.95
       1978 |          1        0.67       43.62
       1979 |          2        1.34       44.97
       1980 |          4        2.68       47.65
       1981 |          3        2.01       49.66
       1983 |          2        1.34       51.01
       1984 |          1        0.67       51.68
       1985 |          1        0.67       52.35
       1986 |          1        0.67       53.02
       1987 |          3        2.01       55.03
       1988 |          1        0.67       55.70
       1989 |          3        2.01       57.72
       1990 |          2        1.34       59.06
       1991 |          3        2.01       61.07
       1992 |          3        2.01       63.09
       1994 |          2        1.34       64.43
       1995 |          2        1.34       65.77
       1996 |          4        2.68       68.46
       1997 |          1        0.67       69.13
       1999 |          3        2.01       71.14
       2000 |          6        4.03       75.17
       2001 |          1        0.67       75.84
       2002 |          1        0.67       76.51
       2003 |          2        1.34       77.85
       2006 |          2        1.34       79.19
       2008 |          2        1.34       80.54
       2009 |          2        1.34       81.88
       2010 |          2        1.34       83.22
       2011 |          2        1.34       84.56
       2012 |          5        3.36       87.92
       2013 |          1        0.67       88.59
       2014 |          4        2.68       91.28
       2015 |          2        1.34       92.62
       2016 |          1        0.67       93.29
       2017 |          1        0.67       93.96
       2019 |          2        1.34       95.30
       2020 |          1        0.67       95.97
       2021 |          6        4.03      100.00
------------+-----------------------------------
      Total |        149      100.00

.                         drop if _merge==2
(149 observations deleted)

.                         rename _merge merge5

.                         recode coup* (.=0) if year>=1950 & year<=2010
(4186 changes made to coup)
(4186 changes made to coupA)
(4186 changes made to coupS)

.                         sort cow year

.                         save temp,replace
file temp.dta saved

.                         merge cow year using nelda-merge
(you are using old merge syntax; see [D] merge for new syntax)

.                         tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      3,681       64.34       64.34
          2 |      1,130       19.75       84.09
          3 |        910       15.91      100.00
------------+-----------------------------------
      Total |      5,721      100.00

.                         drop if _merge==2
(1,130 observations deleted)

.                         rename _merge merge6

.                         rename nelda_country Nelda_country

.                         recode nelda* (.=0) if year>=1945 & year<=2010
(3681 changes made to nelda_election)
(3681 changes made to nelda_relection)
(3681 changes made to nelda_irelection)
(3681 changes made to nelda_mparty)
(3681 changes made to nelda_incumb)
(3681 changes made to nelda_boycott)
(3681 changes made to nelda_mpinc_boycott)
(3681 changes made to nelda_maxmonth)
(3681 changes made to nelda_minmonth)
(3681 changes made to nelda_1)
(3681 changes made to nelda_2)
(3681 changes made to nelda_3)
(3681 changes made to nelda_4)
(3681 changes made to nelda_5)
(3681 changes made to nelda_6)
(3681 changes made to nelda_7)
(3681 changes made to nelda_8)
(3681 changes made to nelda_9)
(3681 changes made to nelda_10)
(3681 changes made to nelda_11)
(3681 changes made to nelda_12)
(3681 changes made to nelda_13)
(3681 changes made to nelda_14)
(3681 changes made to nelda_15)
(3681 changes made to nelda_16)
(3681 changes made to nelda_17)
(3681 changes made to nelda_18)
(3681 changes made to nelda_19)
(3681 changes made to nelda_20)
(3681 changes made to nelda_21)
(3681 changes made to nelda_22)
(3681 changes made to nelda_23)
(3681 changes made to nelda_24)
(3681 changes made to nelda_25)
(3681 changes made to nelda_26)
(3681 changes made to nelda_27)
(3681 changes made to nelda_28)
(3681 changes made to nelda_29)
(3681 changes made to nelda_30)
(3681 changes made to nelda_31)
(3681 changes made to nelda_32)
(3681 changes made to nelda_33)
(3681 changes made to nelda_34)
(3681 changes made to nelda_35)
(3681 changes made to nelda_36)
(3681 changes made to nelda_37)
(3681 changes made to nelda_38)
(3681 changes made to nelda_39)
(3681 changes made to nelda_40)
(3681 changes made to nelda_41)
(3681 changes made to nelda_42)
(3681 changes made to nelda_43)
(3681 changes made to nelda_44)
(3681 changes made to nelda_45)
(3681 changes made to nelda_46)
(3681 changes made to nelda_47)
(3681 changes made to nelda_48)
(3681 changes made to nelda_49)
(3681 changes made to nelda_50)
(3681 changes made to nelda_51)
(3681 changes made to nelda_52)
(3681 changes made to nelda_53)
(3681 changes made to nelda_54)
(3681 changes made to nelda_55)
(3681 changes made to nelda_56)
(3681 changes made to nelda_57)
(3681 changes made to nelda_58)

.                         rename Nelda_country nelda_country

.                         sort cow year

.                         save temp,replace
file temp.dta saved

.                         merge cow year using prio-mergeB
(you are using old merge syntax; see [D] merge for new syntax)

.                         tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      3,646       68.51       68.51
          2 |        731       13.74       82.24
          3 |        945       17.76      100.00
------------+-----------------------------------
      Total |      5,322      100.00

.                         drop if _merge==2
(731 observations deleted)

.                         rename _merge merge7

.                         rename prio_country Prio_country

.                         recode prio* (.=0) if year>=1945 & year<=2010
(3646 changes made to prio_conflict_intra)
(3646 changes made to prio_conflict_inter)
(3651 changes made to prio_conflict_duration_intra)
(3651 changes made to prio_conflict_duration_inter)
(3649 changes made to prio_conflict_cumint_intra)
(3648 changes made to prio_conflict_cumint_inter)
(3649 changes made to prio_conflict_int_intra)
(3648 changes made to prio_conflict_int_inter)
(3651 changes made to prio_lconflict_int_intra)
(3651 changes made to prio_lconflict_int_inter)
(3651 changes made to prio_lconflict_intra)
(3651 changes made to prio_lconflict_inter)
(3651 changes made to prio_lconflict_duration_intra)
(3651 changes made to prio_lconflict_duration_inter)
(3651 changes made to prio_lconflict_cumint_intra)
(3651 changes made to prio_lconflict_cumint_inter)

.                         rename Prio_country prio_country

.                         sort cow year

.                         save temp,replace
file temp.dta saved

.                         merge cow year using nmc-merge
(you are using old merge syntax; see [D] merge for new syntax)

.                         tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          3 |      4,591      100.00      100.00
------------+-----------------------------------
      Total |      4,591      100.00

.                         drop if _merge==2
(0 observations deleted)

.                         rename _merge merge9

.                         sort cow year

.                         save temp,replace
file temp.dta saved

.                         merge cow year using GWFtscs
(you are using old merge syntax; see [D] merge for new syntax)

.                         tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          3 |      4,591      100.00      100.00
------------+-----------------------------------
      Total |      4,591      100.00

.                         rename _merge merge10

.                         sort cow year

.                         merge cow year using latent-protest-data
(you are using old merge syntax; see [D] merge for new syntax)

.                         tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        397        4.57        4.57
          2 |      4,089       47.11       51.68
          3 |      4,194       48.32      100.00
------------+-----------------------------------
      Total |      8,680      100.00

.                         rename _merge merge11

.                         sort cow year

.                         merge cow year using nvc-merge
(you are using old merge syntax; see [D] merge for new syntax)

.                         tab _merge   

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        943       10.10       10.10
          2 |        660        7.07       17.16
          3 |      7,737       82.84      100.00
------------+-----------------------------------
      Total |      9,340      100.00

.                         tab gwf_country if _merge==1  

          Country name |      Freq.     Percent        Cum.
-----------------------+-----------------------------------
           Afghanistan |          9        2.48        2.48
               Albania |          9        2.48        4.96
             Argentina |          4        1.10        6.06
               Bolivia |          9        2.48        8.54
              Bulgaria |          9        2.48       11.02
              Cambodia |          1        0.28       11.29
                 China |          5        1.38       12.67
              Colombia |          5        1.38       14.05
            Costa Rica |          1        0.28       14.33
                  Cuba |          2        0.55       14.88
        Czechoslovakia |          6        1.65       16.53
         Dominican Rep |          9        2.48       19.01
               Ecuador |          2        0.55       19.56
                 Egypt |          9        2.48       22.04
           El Salvador |          9        2.48       24.52
              Ethiopia |          9        2.48       27.00
          Germany East |          6        1.65       28.65
                 Haiti |          5        1.38       30.03
              Honduras |          9        2.48       32.51
               Hungary |          7        1.93       34.44
             Indonesia |          5        1.38       35.81
                  Iran |          9        2.48       38.29
                  Iraq |          9        2.48       40.77
                Jordan |          8        2.20       42.98
           Korea North |          6        1.65       44.63
           Korea South |          6        1.65       46.28
               Liberia |          9        2.48       48.76
                 Libya |          3        0.83       49.59
                Mexico |          9        2.48       52.07
              Mongolia |          9        2.48       54.55
                 Nepal |          9        2.48       57.02
             Nicaragua |          9        2.48       59.50
                  Oman |          9        2.48       61.98
              Pakistan |          7        1.93       63.91
                Panama |          3        0.83       64.74
              Paraguay |          9        2.48       67.22
                  Peru |          6        1.65       68.87
                Poland |          9        2.48       71.35
              Portugal |          9        2.48       73.83
               Romania |          9        2.48       76.31
          Saudi Arabia |          9        2.48       78.79
          South Africa |          9        2.48       81.27
           South Yemen |          1        0.28       81.54
          Soviet Union |          9        2.48       84.02
                 Spain |          9        2.48       86.50
                 Syria |          6        1.65       88.15
                Taiwan |          5        1.38       89.53
              Thailand |          9        2.48       92.01
                Turkey |          5        1.38       93.39
             Venezuela |          6        1.65       95.04
                 Yemen |          9        2.48       97.52
            Yugoslavia |          9        2.48      100.00
-----------------------+-----------------------------------
                 Total |        363      100.00

.                         rename _merge merge12

.                         tab year if merge12==1

       year |      Freq.     Percent        Cum.
------------+-----------------------------------
       1946 |         32        3.39        3.39
       1947 |         32        3.39        6.79
       1948 |         32        3.39       10.18
       1949 |         38        4.03       14.21
       1950 |         44        4.67       18.88
       1951 |         44        4.67       23.54
       1952 |         45        4.77       28.31
       1953 |         46        4.88       33.19
       1954 |         48        5.09       38.28
       1955 |          4        0.42       38.71
       1956 |          4        0.42       39.13
       1957 |          4        0.42       39.55
       1958 |          4        0.42       39.98
       1959 |          4        0.42       40.40
       1960 |          4        0.42       40.83
       1961 |          4        0.42       41.25
       1962 |          4        0.42       41.68
       1963 |          4        0.42       42.10
       1964 |          5        0.53       42.63
       1965 |          7        0.74       43.37
       1966 |          7        0.74       44.11
       1967 |          7        0.74       44.86
       1968 |          7        0.74       45.60
       1969 |          7        0.74       46.34
       1970 |          7        0.74       47.08
       1971 |          7        0.74       47.83
       1972 |          6        0.64       48.46
       1973 |          6        0.64       49.10
       1974 |          7        0.74       49.84
       1975 |          8        0.85       50.69
       1976 |          9        0.95       51.64
       1977 |          9        0.95       52.60
       1978 |         10        1.06       53.66
       1979 |         11        1.17       54.83
       1980 |         10        1.06       55.89
       1981 |         11        1.17       57.05
       1982 |         11        1.17       58.22
       1983 |         11        1.17       59.38
       1984 |         12        1.27       60.66
       1985 |         18        1.91       62.57
       1986 |         19        2.01       64.58
       1987 |         19        2.01       66.60
       1988 |         19        2.01       68.61
       1989 |         19        2.01       70.63
       1990 |         20        2.12       72.75
       1991 |         12        1.27       74.02
       1992 |         14        1.48       75.50
       1993 |         12        1.27       76.78
       1994 |         12        1.27       78.05
       1995 |         13        1.38       79.43
       1996 |         13        1.38       80.81
       1997 |         13        1.38       82.18
       1998 |         13        1.38       83.56
       1999 |         13        1.38       84.94
       2000 |         13        1.38       86.32
       2001 |         13        1.38       87.70
       2002 |         12        1.27       88.97
       2003 |         12        1.27       90.24
       2004 |         12        1.27       91.52
       2005 |         12        1.27       92.79
       2006 |         13        1.38       94.17
       2007 |         13        1.38       95.55
       2008 |         14        1.48       97.03
       2009 |         14        1.48       98.52
       2010 |         14        1.48      100.00
------------+-----------------------------------
      Total |        943      100.00

.                         tab gwf_country if merge12==1 & year==1990 /* 1990 for
>  South Yemen and East Germany not in MEC data */

          Country name |      Freq.     Percent        Cum.
-----------------------+-----------------------------------
          Germany East |          1       50.00       50.00
           South Yemen |          1       50.00      100.00
-----------------------+-----------------------------------
                 Total |          2      100.00

.                         sort cow year   

.                         merge cow year using counter-merge
(you are using old merge syntax; see [D] merge for new syntax)

.                         tab _merge  if year>=1970 

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        119        1.41        1.41
          2 |      1,188       14.05       15.45
          3 |      7,150       84.55      100.00
------------+-----------------------------------
      Total |      8,457      100.00

.                         rename _merge merge13

.                         list gwf_country year if merge13==1 & year>=1970

       +-----------------+
       | gwf_co~y   year |
       |-----------------|
 2722. |            1992 |
 2744. |            1992 |
 2940. |            2006 |
 2941. |            2007 |
 2944. |            2010 |
       |-----------------|
 2945. |            1991 |
 2946. |            1992 |
 2968. |            1991 |
 3081. |            2008 |
 3082. |            2009 |
       |-----------------|
 3083. |            2010 |
 3084. |            1991 |
 3447. |            1985 |
 3448. |            1986 |
 3449. |            1987 |
       |-----------------|
 3450. |            1988 |
 3451. |            1989 |
 3452. |            1990 |
 3476. |            1985 |
 3477. |            1986 |
       |-----------------|
 3478. |            1987 |
 3479. |            1988 |
 3480. |            1989 |
 3481. |            1990 |
 3505. |            1985 |
       |-----------------|
 3506. |            1986 |
 3507. |            1987 |
 3508. |            1988 |
 3509. |            1989 |
 3510. |            1990 |
       |-----------------|
 3557. |            1985 |
 3558. |            1986 |
 3559. |            1987 |
 3560. |            1988 |
 3561. |            1989 |
       |-----------------|
 3562. |            1990 |
 3586. |            1985 |
 3587. |            1986 |
 3588. |            1987 |
 3589. |            1988 |
       |-----------------|
 3590. |            1989 |
 3591. |            1990 |
 3638. |            1985 |
 3639. |            1986 |
 3640. |            1987 |
       |-----------------|
 3641. |            1988 |
 3642. |            1989 |
 3643. |            1990 |
 5912. |            1976 |
 5913. |            1977 |
       |-----------------|
 5914. |            1978 |
 5915. |            1979 |
 5916. |            1980 |
 5917. |            1981 |
 5918. |            1982 |
       |-----------------|
 5919. |            1983 |
 5920. |            1984 |
 5921. |            1985 |
 5922. |            1986 |
 5923. |            1987 |
       |-----------------|
 5924. |            1988 |
 5925. |            1989 |
 7403. |     Oman   1970 |
 8089. |            1970 |
 8199. |            1971 |
       |-----------------|
 8997. |            1995 |
 8998. |            1996 |
 8999. |            1997 |
 9000. |            1998 |
 9001. |            1999 |
       |-----------------|
 9002. |            2000 |
 9003. |            2001 |
 9004. |            2002 |
 9005. |            2003 |
 9008. |            2006 |
       |-----------------|
 9009. |            2007 |
 9012. |            2010 |
 9013. |            2011 |
 9253. |            1986 |
 9254. |            1987 |
       |-----------------|
 9255. |            1988 |
 9256. |            1989 |
 9257. |            1990 |
 9258. |            1991 |
 9259. |            1992 |
       |-----------------|
 9260. |            1993 |
 9261. |            1994 |
 9262. |            1995 |
 9263. |            1996 |
 9264. |            1997 |
       |-----------------|
 9265. |            1998 |
 9313. |            1970 |
 9314. |            1971 |
 9315. |            1972 |
 9316. |            1973 |
       |-----------------|
 9317. |            1974 |
 9318. |            1975 |
 9319. |            1976 |
 9320. |            1977 |
 9321. |            1978 |
       |-----------------|
 9322. |            1979 |
 9323. |            1980 |
 9324. |            1981 |
 9325. |            1982 |
 9326. |            1983 |
       |-----------------|
 9327. |            1984 |
 9328. |            1985 |
 9329. |            1986 |
 9330. |            1987 |
 9331. |            1988 |
       |-----------------|
 9332. |            1989 |
 9333. |            2006 |
 9334. |            2007 |
 9335. |            2008 |
 9336. |            2009 |
       |-----------------|
 9337. |            2010 |
 9338. |            2011 |
 9339. |            2012 |
 9340. |            2013 |
       +-----------------+

.                         keep if gwf_caseid~=.
(5,937 observations deleted)

.                         sort cow year

.                         merge cow year using renavco-region-merge
(you are using old merge syntax; see [D] merge for new syntax)

.                         tab _merge   

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         29        0.30        0.30
          2 |      5,216       53.19       53.48
          3 |      4,562       46.52      100.00
------------+-----------------------------------
      Total |      9,807      100.00

.                         rename _merge merge14

.                         tab gwf_casename if merge14==1

            Regime-case name |      Freq.     Percent        Cum.
-----------------------------+-----------------------------------
         Germany, East 49-90 |          4       13.79       13.79
                Oman 1741-NA |         25       86.21      100.00
-----------------------------+-----------------------------------
                       Total |         29      100.00

.                         tab year if merge12==1 & cowcode==265  /* East Germany
>  1950--1953 */

       year |      Freq.     Percent        Cum.
------------+-----------------------------------
       1950 |          1       16.67       16.67
       1951 |          1       16.67       33.33
       1952 |          1       16.67       50.00
       1953 |          1       16.67       66.67
       1954 |          1       16.67       83.33
       1990 |          1       16.67      100.00
------------+-----------------------------------
      Total |          6      100.00

.                         tab year if merge12==1 & gwf_country=="Oman"  /* 1946-
> -1970 */

       year |      Freq.     Percent        Cum.
------------+-----------------------------------
       1946 |          1       11.11       11.11
       1947 |          1       11.11       22.22
       1948 |          1       11.11       33.33
       1949 |          1       11.11       44.44
       1950 |          1       11.11       55.56
       1951 |          1       11.11       66.67
       1952 |          1       11.11       77.78
       1953 |          1       11.11       88.89
       1954 |          1       11.11      100.00
------------+-----------------------------------
      Total |          9      100.00

.                         keep if gwf_caseid~=.
(5,216 observations deleted)

.                         sort cow year

.                         merge cow year using debruin-merge
(you are using old merge syntax; see [D] merge for new syntax)

.                         tab _merge   

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      1,796       26.26       26.26
          2 |      2,248       32.87       59.13
          3 |      2,795       40.87      100.00
------------+-----------------------------------
      Total |      6,839      100.00

.                         rename _merge merge15

.                         tab gwf_casename if merge15==1

            Regime-case name |      Freq.     Percent        Cum.
-----------------------------+-----------------------------------
           Afghanistan 29-73 |         14        0.78        0.78
               Albania 44-91 |         46        2.56        3.34
             Argentina 43-46 |          1        0.06        3.40
             Argentina 51-55 |          4        0.22        3.62
             Argentina 55-58 |          3        0.17        3.79
             Argentina 58-66 |          1        0.06        3.84
               Armenia 94-98 |          4        0.22        4.06
               Armenia 98-NA |         12        0.67        4.73
               Bolivia 43-46 |          1        0.06        4.79
               Bolivia 46-51 |          5        0.28        5.07
               Bolivia 51-52 |          1        0.06        5.12
               Bolivia 52-64 |          7        0.39        5.51
              Bulgaria 44-90 |         14        0.78        6.29
          Burkina Faso 60-66 |          6        0.33        6.63
          Burkina Faso 66-80 |         14        0.78        7.41
          Burkina Faso 80-82 |          2        0.11        7.52
          Burkina Faso 82-87 |          5        0.28        7.80
          Burkina Faso 87-NA |         23        1.28        9.08
              Cambodia 53-70 |          6        0.33        9.41
                 China 49-NA |         10        0.56        9.97
              Colombia 49-53 |          4        0.22       10.19
              Colombia 53-58 |          5        0.28       10.47
            Congo-Brz  97-NA |         13        0.72       11.19
             Congo-Brz 60-63 |          3        0.17       11.36
             Congo-Brz 63-68 |          5        0.28       11.64
             Congo-Brz 68-91 |         23        1.28       12.92
           Congo/Zaire 60-97 |         37        2.06       14.98
           Congo/Zaire 97-NA |         13        0.72       15.70
          Costa Rica 1948-49 |          1        0.06       15.76
                  Cuba 52-59 |          7        0.39       16.15
        Czechoslovakia 48-89 |         11        0.61       16.76
          Domincan Rep 30-62 |         14        0.78       17.54
               Ecuador 44-47 |          2        0.11       17.65
                 Egypt 22-52 |          7        0.39       18.04
                 Egypt 52-NA |          7        0.39       18.43
           El Salvador 31-48 |          3        0.17       18.60
           El Salvador 48-82 |         11        0.61       19.21
               Eritrea 93-NA |         17        0.95       20.16
          Ethiopia 1889-1974 |         14        0.78       20.94
                 Gabon 60-NA |         50        2.78       23.72
                Gambia 65-94 |         29        1.61       25.33
                Gambia 94-NA |         16        0.89       26.22
         Germany, East 49-90 |         10        0.56       26.78
             Guatemala 54-58 |          4        0.22       27.00
             Guatemala 58-63 |          1        0.06       27.06
                Guinea 08-10 |          2        0.11       27.17
                Guinea 58-84 |         26        1.45       28.62
                Guinea 84-08 |         24        1.34       29.96
         Guinea Bissau 02-03 |          1        0.06       30.01
         Guinea Bissau 74-80 |          6        0.33       30.35
         Guinea Bissau 80-99 |         19        1.06       31.40
                 Haiti 41-46 |          1        0.06       31.46
                 Haiti 50-56 |          6        0.33       31.79
                 Haiti 57-86 |          2        0.11       31.90
              Honduras 33-56 |         11        0.61       32.52
               Hungary 47-90 |         12        0.67       33.18
             Indonesia 49-66 |         10        0.56       33.74
                  Iran 25-79 |         14        0.78       34.52
                  Iraq 32-58 |         13        0.72       35.24
                  Iraq 58-63 |          1        0.06       35.30
                Jordan 46-NA |         13        0.72       36.02
                 Kenya 63-02 |         39        2.17       38.20
           Korea North 48-NA |         11        0.61       38.81
           Korea South 48-60 |         11        0.61       39.42
                Kuwait 61-NA |         49        2.73       42.15
            Kyrgyzstan 05-10 |          5        0.28       42.43
            Kyrgyzstan 91-05 |         14        0.78       43.21
                  Laos 59-60 |          1        0.06       43.26
                  Laos 60-62 |          2        0.11       43.37
                  Laos 75-NA |         35        1.95       45.32
               Lesotho 70-86 |         16        0.89       46.21
               Lesotho 86-93 |          7        0.39       46.60
               Liberia 44-80 |         14        0.78       47.38
                 Libya 51-69 |          8        0.45       47.83
              Madagascar 09- |          1        0.06       47.88
            Madagascar 60-72 |         12        0.67       48.55
            Madagascar 72-75 |          3        0.17       48.72
            Madagascar 75-93 |         18        1.00       49.72
                Malawi 64-94 |         30        1.67       51.39
              Malaysia 57-NA |          2        0.11       51.50
            Mauritania 05-07 |          2        0.11       51.61
            Mauritania 08-NA |          2        0.11       51.73
            Mauritania 60-78 |         18        1.00       52.73
            Mauritania 78-05 |         27        1.50       54.23
                Mexico 15-00 |         14        0.78       55.01
              Mongolia 21-93 |         14        0.78       55.79
               Morocco 56-NA |          3        0.17       55.96
               Myanmar 58-60 |          1        0.06       56.01
             Namibia 1990-NA |         20        1.11       57.13
             Nepal 1846-1951 |          6        0.33       57.46
                 Nepal 51-91 |          8        0.45       57.91
             Nicaragua 36-79 |         14        0.78       58.69
                Oman 1741-NA |         65        3.62       62.31
              Pakistan 47-58 |         11        0.61       62.92
              Pakistan 58-71 |          1        0.06       62.97
                Panama 49-51 |          2        0.11       63.08
                Panama 53-55 |          2        0.11       63.20
              Paraguay 40-48 |          3        0.17       63.36
              Paraguay 48-54 |          6        0.33       63.70
              Paraguay 54-93 |         39        2.17       65.87
                  Peru 48-56 |          8        0.45       66.31
                Poland 44-89 |         14        0.78       67.09
              Portugal 26-74 |         14        0.78       67.87
               Romania 45-89 |         14        0.78       68.65
          Saudi Arabia 27-NA |         14        0.78       69.43
               Senegal 60-00 |         40        2.23       71.66
               Somalia 69-91 |         22        1.22       72.88
          South Africa 10-94 |         14        0.78       73.66
         South Vietnam 54-63 |          9        0.50       74.16
         South Vietnam 63-75 |         12        0.67       74.83
           South Yemen 67-90 |         23        1.28       76.11
          Soviet Union 17-91 |         14        0.78       76.89
                 Spain 39-76 |         14        0.78       77.67
                 Sudan 58-64 |          1        0.06       77.73
                 Syria 46-47 |          1        0.06       77.78
                 Syria 49-51 |          2        0.11       77.90
                 Syria 51-54 |          3        0.17       78.06
                 Syria 57-58 |          1        0.06       78.12
                Taiwan 49-00 |         10        0.56       78.67
              Thailand 06-07 |          1        0.06       78.73
              Thailand 44-47 |          2        0.11       78.84
              Thailand 47-57 |         10        0.56       79.40
              Thailand 57-73 |         16        0.89       80.29
              Thailand 76-88 |         12        0.67       80.96
              Thailand 91-92 |          1        0.06       81.01
                  Togo 60-63 |          3        0.17       81.18
                  Togo 63-NA |         47        2.62       83.80
               Tunisia 56-NA |          3        0.17       83.96
                Turkey 23-50 |          5        0.28       84.24
                Turkey 57-60 |          2        0.11       84.35
          Turkmenistan 91-NA |         19        1.06       85.41
                   UAE 71-NA |         39        2.17       87.58
               Uruguay 73-84 |         11        0.61       88.20
            Uzbekistan 91-NA |         19        1.06       89.25
             Venezuela 48-58 |         10        0.56       89.81
               Vietnam 54-NA |         56        3.12       92.93
                 Yemen 18-62 |         17        0.95       93.88
                 Yemen 62-67 |          5        0.28       94.15
                 Yemen 67-74 |          7        0.39       94.54
                 Yemen 74-78 |          4        0.22       94.77
                 Yemen 78-NA |         12        0.67       95.43
            Yugoslavia 44-90 |         14        0.78       96.21
                Zambia 67-91 |         24        1.34       97.55
                Zambia 96-NA |         14        0.78       98.33
              Zimbabwe 80-NA |         30        1.67      100.00
-----------------------------+-----------------------------------
                       Total |      1,796      100.00

.                         keep if gwf_caseid~=.
(2,248 observations deleted)

.                         sort cow year

.                         merge cow year using vdem-merge
(you are using old merge syntax; see [D] merge for new syntax)
(variable ccode was int, now long to accommodate using data's values)
(variable cowcode was float, now double to accommodate using data's values)

.                         tab _merge   

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |          1        0.00        0.00
          2 |     21,845       82.63       82.64
          3 |      4,590       17.36      100.00
------------+-----------------------------------
      Total |     26,436      100.00

.                         rename _merge merge16

.                         tab gwf_casename if merge16==1

            Regime-case name |      Freq.     Percent        Cum.
-----------------------------+-----------------------------------
                 Yemen 78-NA |          1      100.00      100.00
-----------------------------+-----------------------------------
                       Total |          1      100.00

.                         drop if merge16==2
(21,845 observations deleted)

.                         keep if gwf_caseid~=.
(0 observations deleted)

.                         xtset cow year

Panel variable: cowcode (unbalanced)
 Time variable: year, 1946 to 2010, but with gaps
         Delta: 1 unit

.                         saveold temp,replace
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in
      older Stata formats)
file temp.dta saved

.         
.                 * Variable transformations *
.                         use temp,clear

.                         qui sum latentmean 

.                         gen repression = (latentmean - r(mean))/r(sd)  /*stand
> ardize within sample */
(122 missing values generated)

.                         replace repression = repression*-1   /* flip scale */
(4,469 real changes made)

.                         gen lt = ln(gwf_leader_duration)

.                         gen civwar = prio_lconflict_intra>0  & prio_lconflict_
> int_intra==2

.                         gen intwar = prio_lconflict_inter>0  & prio_lconflict_
> int_inter==2

.                         xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                         gen coup12 =  l1.coupA==1 | l2.coupA==1  | l1.coupS==1
>  | l2.coupS==1 if year>=1950
(134 missing values generated)

.                         gen election = nelda_mpinc_boycott 

.                         tab gwf_prior

Regime type of the country |
   prior to regime seizing |
                     power |      Freq.     Percent        Cum.
---------------------------+-----------------------------------
                 democracy |        875       19.06       19.06
          dictatorship_mil |        372        8.10       27.16
       dictatorship_nonmil |      1,286       28.01       55.17
          foreign-occupied |        405        8.82       63.99
           not-independent |      1,494       32.54       96.54
                   warlord |        159        3.46      100.00
---------------------------+-----------------------------------
                     Total |      4,591      100.00

.                         gen priordem = gwf_prior=="democracy"

.                         gen institutions = (support*legcomp)/8  /* 0-1 scale *
> /

.                         
.                 * Decade dummies *
.                         gen d1960 = year>=1960 & year<1970

.                         gen d1970 = year>=1970 & year<1980

.                         gen d1980 = year>=1980 & year<1990

.                         gen d1990 = year>=1990 & year<2000

.                         gen time = year-1946

.                         gen time2 = time^2

.                         gen time3 = time^3

.                         gen d2000 = year>=2000 & year<=2010

.                         
.                 * Save data *
.                         xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                         keep if gwf_caseid~=.
(0 observations deleted)

.                         sort cow year

.                         saveold temp, replace
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in
      older Stata formats)
file temp.dta saved

.                 
.                 * RENAVCO, NAVCO, and MEC data *
.                         use "$dir\RENAVCO_nonviolent campaigns_crosswalk_gwfau
> tocracies.dta" ,clear

.                         tab byear if eyear==.

 First Year |
of Campaign |      Freq.     Percent        Cum.
------------+-----------------------------------
       1979 |          1       20.00       20.00
       2007 |          1       20.00       40.00
       2010 |          1       20.00       60.00
       2011 |          1       20.00       80.00
       2013 |          1       20.00      100.00
------------+-----------------------------------
      Total |          5      100.00

.                         recode eyear (.=2015)  /* right censored */
(5 changes made to eyear)

.                         gen spell =  eyear-byear +1

.                         expand spell
(292 observations created)

.                         sort id

.                         gen n = _n

.                         egen min =min(n),by(id)

.                         gen year=byear if min==n
(292 missing values generated)

.                         sort id

.                         bysort id: replace year = year[_n-1] +1 if year==.
(292 real changes made)

.                         gen xonset = byear==year

.                         gen reduration = 1 if xonset==1
(292 missing values generated)

.                         gen cowcode = ccode

.                         sort id year

.                         replace reduration = reduration[_n-1]+1 if reduration=
> =.
(292 real changes made)

.                         egen max = min(reduration), by(cow year)

.                         replace reduration = max
(60 real changes made)

.                         egen maxo = max(xonset),by(cow year)

.                         replace xonset=maxo
(34 real changes made)

.                         gen lreduration =ln(reduration)

.                         gen xongoing = 1

.                         sort cow year

.                         sort cow year

.                         gen repeat = year==year[_n-1] & cow==cow[_n-1]

.                         tab repeat

     repeat |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        441       85.63       85.63
          1 |         74       14.37      100.00
------------+-----------------------------------
      Total |        515      100.00

.                         egen r = max(repeat),by(cow year)

.                         egen mm =mean(byear),by(cow year)

.                         listtex cow target year campaign if r==1 & byear==year
>  & mm==year using NVC-multiples.tex, rstyle(tabular) ///
>                                 head("\begin{tabular}{l l c l}"" \textit{Count
> ry code} & \textit{Target} & \textit{Year} & \textit{Campaign}  \\\\") ///
>                                 foot("\end{tabular}") replace   
(file NVC-multiples.tex not found)

.                                 gen c = campaign if xonset==1 & byear==year
(297 missing values generated)

.                         list cow year target campaign byear if r==1 & c=="",cl
> ean noobs

    cowcode   year              target                                 campaign 
>   byear  
         41   1986     Duvalier regime                      Operation Déchoukaj 
>    1985  
        145   1979     Military regime                      Bolivian Anti-Junta 
>    1978  
        230   1976               Spain                Catalan autonomy movement 
>    1975  
        230   1977               Spain                    The Citizens movement 
>    1976  
        230   1977               Spain                Catalan autonomy movement 
>    1975  
        339   1991    Communist regime                   Albania Anti-Communist 
>    1990  
        345   1988                                                              
>    1988  
        345   1989       Yugoslav rule                       Kosovo Albania III 
>    1988  
        345   1989                                                              
>    1988  
        345   1990                                                              
>    1988  
        345   1990       Yugoslav rule                       Kosovo Albania III 
>    1988  
        345   1991    Milosevic regime    Democratic Movement of Serbia (DEPOS) 
>    1990  
        345   1991       Yugoslav rule                       Kosovo Albania III 
>    1988  
        345   1991                                                              
>    1988  
        345   1992    Milosevic regime    Democratic Movement of Serbia (DEPOS) 
>    1990  
        345   1992                                                              
>    1988  
        345   1992       Yugoslav rule                       Kosovo Albania III 
>    1988  
        345   1996       Yugoslav rule                       Kosovo Albania III 
>    1988  
        345   1997       Yugoslav rule                       Kosovo Albania III 
>    1988  
        345   1997    Milosevic regime              Zajedno (Together) Protests 
>    1996  
        355   1990    Communist regime                  Bulgaria Anti-Communist 
>    1989  
        365   1988           USSR rule                       Singing Revolution 
>    1987  
        365   1989    Communist Regime                         Democratic Union 
>    1988  
        365   1989           USSR rule    Sajudis / Lithuanian pro-dem movement 
>    1988  
        365   1989           USSR rule                  Karabakh Movement / ANM 
>    1988  
        365   1989           USSR rule                  Latvia pro-dem movement 
>    1988  
        365   1989           USSR rule                       Singing Revolution 
>    1987  
        365   1990    Communist Regime                         Democratic Union 
>    1988  
        365   1990           USSR rule                 Azerbaijan Popular Front 
>    1989  
        365   1990           USSR rule                  Latvia pro-dem movement 
>    1988  
        365   1990           USSR rule                     Georgia secessionist 
>    1989  
        365   1990           USSR rule                     Ukraine secessionist 
>    1989  
        365   1990           USSR rule                       Singing Revolution 
>    1987  
        365   1990           USSR rule                  Karabakh Movement / ANM 
>    1988  
        365   1990           USSR rule                    Moldova Popular Front 
>    1989  
        365   1990           USSR rule    Sajudis / Lithuanian pro-dem movement 
>    1988  
        365   1991           USSR rule                     Georgia secessionist 
>    1989  
        365   1991           USSR rule                  Latvia pro-dem movement 
>    1988  
        365   1991           USSR rule                 Azerbaijan Popular Front 
>    1989  
        365   1991           USSR rule                       Singing Revolution 
>    1987  
        365   1991    Communist Regime                         Democratic Union 
>    1988  
        365   1991           USSR rule                     Ukraine secessionist 
>    1989  
        365   1991           USSR rule                  Karabakh Movement / ANM 
>    1988  
        365   1991           USSR rule    Sajudis / Lithuanian pro-dem movement 
>    1988  
        365   1991           USSR rule                    Moldova Popular Front 
>    1989  
        475   1993       Nigerian rule                           Ogoni movement 
>    1990  
        475   1994       Military rule                    Nigeria Anti-Military 
>    1993  
        475   1994       Nigerian rule                           Ogoni movement 
>    1990  
        475   1995       Nigerian rule                           Ogoni movement 
>    1990  
        475   1995       Military rule                    Nigeria Anti-Military 
>    1993  
        600   2011       Moroccan rule           Sahrawi independence intifidas 
>    1999  
        600   2012   Bouteflika regime                 The February 20 movement 
>    2011  
        600   2012       Moroccan rule           Sahrawi independence intifidas 
>    1999  
        679   2011        Saleh regime           The Southern Mobility Movement 
>    2007  
        679   2012        Saleh regime             Yemen pro-democracy movement 
>    2011  
        679   2012        Saleh regime           The Southern Mobility Movement 
>    2007  
        690   2012     Al-Sabah Regime              Dignity of a Nation / Irhal 
>    2011  
        710   1989        Chinese rule                    Tibetan resistance II 
>    1987  
        770   1969         Khan regime                                Anti-Khan 
>    1968  
        850   1989                                                              
>    1979  
        850   1990                                                              
>    1979  
        850   1990     Indonesian rule                      Timorese resistance 
>    1989  
        850   1991                                                              
>    1979  
        850   1991     Indonesian rule                      Timorese resistance 
>    1989  
        850   1992     Indonesian rule                      Timorese resistance 
>    1989  
        850   1992                                                              
>    1979  
        850   1993                                                              
>    1979  
        850   1993     Indonesian rule                      Timorese resistance 
>    1989  
        850   1994                                                              
>    1979  
        850   1994     Indonesian rule                      Timorese resistance 
>    1989  
        850   1995     Indonesian rule                      Timorese resistance 
>    1989  
        850   1995                                                              
>    1979  
        850   1996                                                              
>    1979  
        850   1996     Indonesian rule                      Timorese resistance 
>    1989  
        850   1997     Indonesian rule                      Timorese resistance 
>    1989  
        850   1997                                                              
>    1979  
        850   1998     Indonesian rule                      Timorese resistance 
>    1989  
        850   1998      Suharto regime                             Anti-Suharto 
>    1997  
        850   1998                                                              
>    1979  
        850   1999     Indonesian rule                      Timorese resistance 
>    1989  
        850   1999                                                              
>    1979  
        850   2000                                                              
>    1979  
        850   2000     Indonesian rule         Aceh Self-Determination Struggle 
>    1999  
        850   2001                                                              
>    1979  
        850   2001     Indonesian rule         Aceh Self-Determination Struggle 
>    1999  

.                         drop if  r==1 & c==""
(85 observations deleted)

.                         egen tag = tag(cow year)

.                         tab tag

tag(cowcode |
      year) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |          9        2.09        2.09
          1 |        421       97.91      100.00
------------+-----------------------------------
      Total |        430      100.00

.                         keep if tag==1          
(9 observations deleted)

.                         drop tag min max* n r mm repeat c

.                         sort cow year

.                         save "$dir\renavco-merge.dta", replace
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\renavco-merge.dta saved

.  
. 
.         **********************************************************************
> ************************************
.                         use "$dir\renavco-merge.dta", clear

.                         merge cow year using "$dir\temp.dta"
(you are using old merge syntax; see [D] merge for new syntax)
(variable cowcode was float, now double to accommodate using data's values)
(variable year was float, now double to accommodate using data's values)

.                         tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         88        1.88        1.88
          2 |      4,258       91.00       92.88
          3 |        333        7.12      100.00
------------+-----------------------------------
      Total |      4,679      100.00

.                         drop if _merge==1
(88 observations deleted)

.                         drop _merge

.                         keep if gwf_caseid~=.
(0 observations deleted)

.                         recode xonset (.=0)
(4258 changes made to xonset)

.                         recode xongoing (.=0)
(4258 changes made to xongoing)

.                         xtset cow year

Panel variable: cowcode (unbalanced)
 Time variable: year, 1946 to 2010, but with gaps
         Delta: 1 unit

.                         gen lag_xongoing = l.xongoing
(176 missing values generated)

.                         btscs xonset year cow,gen(xyrs)

.                         gen lxyrs  = ln(1+xyrs)

.                         gen xyrs2 = xyrs^2 

.                         gen xyrs3  = xyrs^3

.                         
.                         gen gdem = gwf_fail_subsregime==1

.                         replace gdem =1 if gwf_casename=="Soviet Union 17-91" 
> & year==1991
(1 real change made)

.                         replace gdem =1 if gwf_casename=="Germany East 49-90" 
> & year==1990      
(1 real change made)

. 
.                         gen armed = gwf_fail_type  

.                         recode armed (5=1) (6=1) (7=1) (1=0) (2=0) (3=0) (4=0)
>  (8=0) (9=1) /* coups, rebellions, and foriegn invasions, state collapse */
(223 changes made to armed)

.                         recode armed (1=0) if (cow==652 & year==1958) | (cow==
> 345 & year==1990)  /* Syria joins Egypt; Yugoslav republics declare independen
> ce */
(2 changes made to armed)

.                                 
.         **************************************
.         ********* Look at the sample *********
.         **************************************
.                         **** Plot time trend in combined Onsets ***
.                         egen yr_protest = mean(xonset),by(year)

.                         twoway (bar yr_protest year, sort bcol(gs10)xlab(1950(
> 10)2010)lcol(gs15)) (lpoly yr_ year,bw(2) ///
>                                 lcol(blue)lpat(solid)ytitle("{it:Share of dict
> atorships with }" "{it:Non-violent protest campaigns}", ///
>                                 size(small))legend(lab(1 "% per year") lab(2 "
> Smoothed trend") pos(6)ring(1)col(2)))

.                         graph export "$dir\protest-trend.pdf", as(pdf)   repla
> ce
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\protest-trend.pdf saved as PDF format

. 
.                         **** Estimating sample ****
.                         global cvar ="lxyrs lt lnregion xpers1 xpers2 xpers"

.                         sum xonset 

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      xonset |      4,591    .0396428    .1951398          0          1

.                         reg xonset $cvar  gdem

      Source |       SS           df       MS      Number of obs   =     4,559
-------------+----------------------------------   F(7, 4551)      =     25.58
       Model |   6.6138024         7  .944828914   Prob > F        =    0.0000
    Residual |  168.120569     4,551  .036941457   R-squared       =    0.0379
-------------+----------------------------------   Adj R-squared   =    0.0364
       Total |  174.734372     4,558  .038335755   Root MSE        =     .1922

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       lxyrs |   -.000817   .0029311    -0.28   0.780    -.0065634    .0049295
          lt |   .0087749   .0032683     2.68   0.007     .0023675    .0151823
    lnregion |   .0313171   .0063777     4.91   0.000     .0188138    .0438205
      xpers1 |   .0217148   .0198907     1.09   0.275    -.0172806    .0607103
      xpers2 |   -.014805   .0177918    -0.83   0.405    -.0496855    .0200755
       xpers |  -.0284375   .0283338    -1.00   0.316    -.0839856    .0271105
        gdem |    .221486   .0194076    11.41   0.000     .1834378    .2595343
       _cons |   .0250976   .0082486     3.04   0.002     .0089264    .0412689
------------------------------------------------------------------------------

.                         gen sample=e(sample)==1

.                         tab gwf_casename if sample==0

       Regime type case name |      Freq.     Percent        Cum.
-----------------------------+-----------------------------------
         Germany, East 49-90 |          5       15.63       15.63
           Korea South 48-60 |          1        3.13       18.75
                Oman 1741-NA |         26       81.25      100.00
-----------------------------+-----------------------------------
                       Total |         32      100.00

.                         keep if sample==1
(32 observations deleted)

.                         label var xonset  "Non-viol. protest campaign onset"

.                         label var xpers "Personalization"

.                         label var xpers1  "Party personalization" 

.                         label var xpers2  `""{bf:Security}     " "{bf:personal
> ization}""'

.                         label var lpopl "Population (log)"

.                         label var lt  "Leader tenure (log)" 

.                         label var lnregion `""Region Non-violent " "episode on
> sets (log)""'  

.                         label var gwf_pers  "Personalist regime" 

.                         label var lxyrs "Time since last onset (log)"

.                         label var coldwar "Cold war"

.  
.                         * Sum stats *
.                         *sutex xonset year xpers xpers1 xpers2 xyrs lt lpopl l
> nregion  if sample==1,minmax labels file($dir/SumstatsStandarized.tex) replace
.                         sort gwf_country year

.                         replace navco11 ="No" if navco11==""
(4,408 real changes made)

.                         replace mec ="No" if navco11==""
(0 real changes made)

.                         gen xrenavco="Yes" if renavco==1
(4,246 missing values generated)

.                         replace xrenavco="No" if renavco==0
(20 real changes made)

.                         *listtex gwf_country year campaign renavco navco11 mec
>  if xonset==1 & sample==1 using nvcstarts.tex, rstyle(tabular) ///
>                         *       head("\begin{tabular}{rc l c c c}"" \textit{Co
> untry} & \textit{Year} & \textit{Campaign} & \textit{RENAVCO} & \textit{NAVCO}
>  & \textit{MEC} \\\\") foot("\end{tabular}") replace
.                         sum year

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
        year |      4,559    1979.757    16.52064       1946       2010

.                         egen tag =tag(gwf_caseid) if sample==1

.                         egen max =max(year),by(gwf_caseid)

.                         egen min=min(year),by(gwf_caseid)

.                         * listtex gwf_casename min max if tag==1 using regimes
> list.tex, rstyle(tabular) ///
>                         *       head("\begin{tabular}{l c c c}"" \textit{Regim
> e-case} & \textit{Begin year} & \textit{End year}") ///
>                         *       foot("\end{tabular}") replace
.                         drop tag max min

.                                 
.                         * Raw data *
.                         ttest xpers,by(xonset)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       0 |   4,377    .4224509    .0041896    .2771788    .4142372    .4306647
       1 |     182    .3862132    .0210749    .2843162    .3446291    .4277973
---------+--------------------------------------------------------------------
Combined |   4,559    .4210043    .0041103    .2775261    .4129462    .4290624
---------+--------------------------------------------------------------------
    diff |            .0362377    .0209904               -.0049136    .0773891
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   1.7264
H0: diff = 0                                     Degrees of freedom =     4557

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9578         Pr(|T| > |t|) = 0.0843          Pr(T > t) = 0.0422

.                         ttest xpers1,by(xonset)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       0 |   4,377    .2328586    .0040658    .2689916    .2248875    .2408297
       1 |     182    .2349473    .0200258    .2701629    .1954333    .2744614
---------+--------------------------------------------------------------------
Combined |   4,559     .232942    .0039841     .269009    .2251312    .2407528
---------+--------------------------------------------------------------------
    diff |           -.0020887    .0203528               -.0419902    .0378127
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.1026
H0: diff = 0                                     Degrees of freedom =     4557

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.4591         Pr(|T| > |t|) = 0.9183          Pr(T > t) = 0.5409

.                         ttest xpers2,by(xonset)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       0 |   4,377    .4722563    .0045729    .3025377    .4632911    .4812215
       1 |     182    .4136971    .0226772    .3059322    .3689514    .4584427
---------+--------------------------------------------------------------------
Combined |   4,559    .4699186    .0044854    .3028572     .461125    .4787122
---------+--------------------------------------------------------------------
    diff |            .0585593    .0228973                .0136694    .1034492
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   2.5575
H0: diff = 0                                     Degrees of freedom =     4557

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9947         Pr(|T| > |t|) = 0.0106          Pr(T > t) = 0.0053

.         
.                         * Standardize variables *
.                         local var ="$cvar"

.                         foreach v of local var {
  2.                                 qui sum `v' if sample==1
  3.                                 qui replace `v' = (`v'-r(mean))/r(sd)
  4.                         }

.                 
.                         * Get unit and time means *
.                         egen rmean =mean(xonset) if sample==1,by(gwf_caseid)

.                         gen max = rmean>0 & rmean<1 if rmean~=.

.                         tab max

        max |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,005       43.98       43.98
          1 |      2,554       56.02      100.00
------------+-----------------------------------
      Total |      4,559      100.00

.                         gen fe = max*-1 +1

.                         tab fe max

           |          max
        fe |         0          1 |     Total
-----------+----------------------+----------
         0 |         0      2,554 |     2,554 
         1 |     2,005          0 |     2,005 
-----------+----------------------+----------
     Total |     2,005      2,554 |     4,559 

.                         replace fe = gwf_caseid if max==1
(2,554 real changes made)

.                         local var = "xonset ypers2 xyrs xyrs2 xyrs3 coldwar ld
>  gdem $cvar "

.                         foreach v of local var {
  2.                                 egen mp_`v'=mean(`v') if sample==1,by(fe)
  3.                                 egen m_`v'=mean(`v') if sample==1,by(gwf_ca
> seid)
  4.                                 egen y_`v'=mean(`v') if sample==1,by(year)
  5.                                 gen mXy_`v' = m_`v'*y_`v'
  6.                         }       

.                         
.                         
.                         sort cow year

.                         save "$dir\temp-fe.dta", replace
(file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\temp-fe.dta not found)
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\temp-fe.dta saved

.                         
.                         
.                         * First paragraph stats *
.                         use temp-fe, clear

.                         tab xonset xongoing             /* all onsets are ongo
> ing years */

 Non-viol. |
   protest |
  campaign |       xongoing
     onset |         0          1 |     Total
-----------+----------------------+----------
         0 |     4,226        151 |     4,377 
         1 |         0        182 |       182 
-----------+----------------------+----------
     Total |     4,226        333 |     4,559 

.                         egen m_xongoing =mean(xongoing),by(gwf_caseid)

.                         egen tag =tag(gwf_caseid) if m_xongoing~=.

.                         gen noprotest = m_xongoing==0

.                         tab noprotest if tag==1         /* 58% of regimes neve
> r face a mass protest */

  noprotest |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        117       41.79       41.79
          1 |        163       58.21      100.00
------------+-----------------------------------
      Total |        280      100.00

.                         gen noonset  = m_xonset==0

.                         tab noonset if tag==1           /* 60% of regimes neve
> r have an onset between 1946 and 2010 */

    noonset |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        112       40.00       40.00
          1 |        168       60.00      100.00
------------+-----------------------------------
      Total |        280      100.00

.                         sum xongoing                            /* 7.4% of reg
> ime-years have a protest */

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    xongoing |      4,559    .0730423    .2602345          0          1

.                         
.  *****************************************************************************
> *******
.  ************************************   Analysis   ***************************
> *******
.  *****************************************************************************
> *******
.                 use temp-fe,clear

.                 global cvar = "coldwar lxyrs lt lnregion"

.                 qui centile xpers2 if sample==1,centile(50)

.                 di r(c_1)
-.03042081

.                 gen treat  = xpers2>=-.02472032 if xpers2~=.

.                 tab treat if sample==1

      treat |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,324       50.98       50.98
          1 |      2,235       49.02      100.00
------------+-----------------------------------
      Total |      4,559      100.00

.                 egen m_treat = mean(treat) if sample==1,by(gwf_caseid)

.                 egen min=min(year) if treat==1,by(gwf_caseid)
(2,324 missing values generated)

.                 gen first_treatment =year==min if year~=.

.                 sort cowcode year

.                 keep if year~=. & xpers2~=. & gdem~=.
(0 observations deleted)

.                 save temp-id,replace
(file temp-id.dta not found)
file temp-id.dta saved

.                 
.                 * Show within equivalency in OLS *
.                         xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                                 * unit means *
.                         qui reg xonset lxyrs lt lnregion treat coldwar m_lxyrs
>  m_lt m_lnregion m_coldwar m_treat m_xonset, vce(cluster gwf_caseid)

.                         lincom treat

 ( 1)  treat = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0242605   .0102244    -2.37   0.018    -.0443873   -.0041337
------------------------------------------------------------------------------

.                                 * unit intercepts *
.                         qui reg xonset lxyrs lt lnregion treat coldwar i.gwf_c
> aseid, vce(cluster gwf_caseid)

.                         lincom treat            

 ( 1)  treat = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0242605   .0105459    -2.30   0.022    -.0450201   -.0035009
------------------------------------------------------------------------------

.                                 * fixed effects *
.                         qui xtreg xonset lxyrs lt lnregion treat coldwar,fe vc
> e(cluster gwf_caseid)

.                         lincom treat

 ( 1)  treat = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0242605   .0102177    -2.37   0.018     -.044374    -.004147
------------------------------------------------------------------------------

.                                 * absorbed fixed effects *
.                         qui reghdfe xonset lxyrs lt lnregion treat coldwar,a(g
> wf_caseid) vce(cluster gwf_caseid)

.                         lincom treat

 ( 1)  treat = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0242605   .0102194    -2.37   0.018    -.0443857   -.0041353
------------------------------------------------------------------------------

.                                 * unit means without \bar{Y} *
.                         qui reg xonset lxyrs lt lnregion treat coldwar m_lxyrs
>  m_lt m_lnregion m_coldwar m_treat, vce(cluster gwf_caseid)

.                         lincom treat

 ( 1)  treat = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0242605   .0102233    -2.37   0.018    -.0443851   -.0041359
------------------------------------------------------------------------------

.                         * Nonlinear link *
.                         qui probit xonset lxyrs lt lnregion treat coldwar m_lx
> yrs m_lt m_lnregion m_coldwar m_treat, vce(cluster gwf_caseid)

.                         margins,dydx(treat)

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(xonset), predict()
dy/dx wrt:  treat

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       treat |  -.0378419   .0121954    -3.10   0.002    -.0617445   -.0139393
------------------------------------------------------------------------------

.                         lincom treat

 ( 1)  [xonset]treat = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.5074844   .1611342    -3.15   0.002    -.8233017   -.1916671
------------------------------------------------------------------------------

.                         qui xtprobit xonset lxyrs lt lnregion treat coldwar m_
> lxyrs m_lt m_lnregion m_coldwar m_treat, vce(cluster gwf_caseid)

.                         margins, dydx(treat) predict(pu0)

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(xonset=1 | u_i=0), predict(pu0)
dy/dx wrt:  treat

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       treat |    -.03784   .0129406    -2.92   0.003    -.0632031   -.0124769
------------------------------------------------------------------------------

.                         lincom treat

 ( 1)  [xonset]treat = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.5074791   .1626638    -3.12   0.002    -.8262944   -.1886638
------------------------------------------------------------------------------

.                         
.                          * ECM LRM *
.                         xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                         ivreg  xonset (d.xonset=l.xonset) d.lxyrs lxyrs d.lt l
> t d.lnregion lnregion d.treat treat, cluster(gwf_leaderid)

Instrumental variables 2SLS regression          Number of obs     =      4,279
                                                F(9, 462)         =       4.91
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0863
                                                Root MSE          =      .1885

                         (Std. err. adjusted for 463 clusters in gwf_leaderid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xonset |
         D1. |   .0385306   .0531841     0.72   0.469    -.0659821    .1430433
             |
       lxyrs |
         D1. |  -.0112799   .0170473    -0.66   0.509    -.0447797      .02222
         --. |   -.008697   .0047479    -1.83   0.068    -.0180271    .0006331
             |
          lt |
         D1. |  -.0087383   .0067189    -1.30   0.194    -.0219417    .0044651
         --. |   .0067296    .003228     2.08   0.038     .0003863     .013073
             |
    lnregion |
         D1. |   .0030914   .0029307     1.05   0.292    -.0026679    .0088506
         --. |   .0145513   .0042646     3.41   0.001     .0061709    .0229317
             |
       treat |
         D1. |  -.0214415   .0141397    -1.52   0.130    -.0492275    .0063446
         --. |  -.0104346   .0064966    -1.61   0.109    -.0232011    .0023319
       _cons |   .0466067   .0048625     9.59   0.000     .0370514     .056162
------------------------------------------------------------------------------
Instrumented: D.xonset
 Instruments: D.lxyrs lxyrs D.lt lt D.lnregion lnregion D.treat treat
              L.xonset

.                         ivreg  xonset (d.xonset=l.xonset) d.lxyrs lxyrs d.lt l
> t d.lnregion lnregion d.xpers2 xpers2, cluster(gwf_leaderid)

Instrumental variables 2SLS regression          Number of obs     =      4,279
                                                F(9, 462)         =       5.13
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0917
                                                Root MSE          =     .18794

                         (Std. err. adjusted for 463 clusters in gwf_leaderid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xonset |
         D1. |   .0407879   .0523934     0.78   0.437     -.062171    .1437469
             |
       lxyrs |
         D1. |  -.0120025   .0168048    -0.71   0.475    -.0450259    .0210208
         --. |  -.0080507   .0047959    -1.68   0.094    -.0174751    .0013738
             |
          lt |
         D1. |   -.007958   .0062601    -1.27   0.204    -.0202597    .0043438
         --. |   .0077685   .0032018     2.43   0.016     .0014765    .0140604
             |
    lnregion |
         D1. |   .0030506   .0029169     1.05   0.296    -.0026815    .0087827
         --. |   .0146528   .0042614     3.44   0.001     .0062787    .0230268
             |
      xpers2 |
         D1. |  -.0153143   .0111858    -1.37   0.172    -.0372957    .0066671
         --. |  -.0081081   .0035388    -2.29   0.022    -.0150621    -.001154
       _cons |    .041489   .0033531    12.37   0.000     .0348997    .0480782
------------------------------------------------------------------------------
Instrumented: D.xonset
 Instruments: D.lxyrs lxyrs D.lt lt D.lnregion lnregion D.xpers2 xpers2
              L.xonset

.                         
.                         * LDV-FE * 
.                         xtset cowcode year

Panel variable: cowcode (unbalanced)
 Time variable: year, 1946 to 2010, but with gaps
         Delta: 1 unit

.                         gen l1mean5 = l.mean5
(533 missing values generated)

.                         alpha l1v2cademmob l1mean5 lag_xongoing  l1_nav13_ongo
> ing l1_nav21_ongoing, std item gen(l1protest)

Test scale = mean(standardized items)

                                                            Average
                             Item-test     Item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     correlation     alpha
-------------+-----------------------------------------------------------------
l1v2cademmob | 3189    +       0.5946        0.3585          0.4249      0.7471
l1mean5      | 4026    +       0.6475        0.4188          0.4166      0.7407
lag_xongoing | 4386    +       0.7260        0.5207          0.3731      0.7042
l1_nav13_o~g | 4559    +       0.7753        0.5966          0.3365      0.6698
l1_nav21_o~g | 4559    +       0.7887        0.6166          0.3253      0.6585
-------------+-----------------------------------------------------------------
Test scale   |                                               0.3774      0.7519
-------------------------------------------------------------------------------

.                         gen l2protest = l.l1protest
(176 missing values generated)

.                         reghdfe xonset lxyrs l1protest l2protest lt lnregion t
> reat,a(gwf_caseid year)vce(cluster gwf_leaderid)
(dropped 16 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =      4,367
Absorbing 2 HDFE groups                           F(   6,    465) =       7.22
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1482
                                                  Adj R-squared   =     0.0824
                                                  Within R-sq.    =     0.0165
Number of clusters (gwf_leaderid) =        466    Root MSE        =     0.1869

                         (Std. err. adjusted for 466 clusters in gwf_leaderid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       lxyrs |   .0371357   .0077167     4.81   0.000     .0219717    .0522996
   l1protest |   .0353278   .0113043     3.13   0.002     .0131139    .0575417
   l2protest |  -.0075632   .0100801    -0.75   0.453    -.0273714    .0122451
          lt |   .0064894   .0044568     1.46   0.146    -.0022687    .0152474
    lnregion |   .0131487   .0041128     3.20   0.001     .0050667    .0212307
       treat |  -.0235965   .0101498    -2.32   0.021    -.0435417   -.0036513
       _cons |   .0485053   .0058558     8.28   0.000     .0369981    .0600124
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  gwf_caseid |       245           0         245     |
        year |        64           1          63     |
-----------------------------------------------------+

.                         reghdfe xonset lxyrs l1protest l2protest lt lnregion x
> pers2,a(gwf_caseid year)vce(cluster gwf_leaderid)
(dropped 16 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =      4,367
Absorbing 2 HDFE groups                           F(   6,    465) =       7.32
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1479
                                                  Adj R-squared   =     0.0821
                                                  Within R-sq.    =     0.0162
Number of clusters (gwf_leaderid) =        466    Root MSE        =     0.1869

                         (Std. err. adjusted for 466 clusters in gwf_leaderid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       lxyrs |   .0374558   .0076743     4.88   0.000     .0223753    .0525363
   l1protest |   .0358571   .0113468     3.16   0.002     .0135598    .0581544
   l2protest |  -.0076196   .0100927    -0.75   0.451    -.0274526    .0122134
          lt |   .0067958   .0043629     1.56   0.120    -.0017777    .0153693
    lnregion |   .0132044   .0041201     3.20   0.001     .0051081    .0213007
      xpers2 |  -.0115029   .0066316    -1.73   0.083    -.0245344    .0015287
       _cons |   .0369417   .0022083    16.73   0.000     .0326023    .0412812
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  gwf_caseid |       245           0         245     |
        year |        64           1          63     |
-----------------------------------------------------+

.                         
.                         * FD *
.                         xtset gwf_leaderid year

Panel variable: gwf_leaderid (unbalanced)
 Time variable: year, 1946 to 2010, but with gaps
         Delta: 1 unit

.                         xi:xtivreg2 xonset lxyrs lt lnregion treat,fd cluster(
> gwf_leaderid)  

FIRST DIFFERENCES ESTIMATION
----------------------------
Number of groups =       414                    Obs per group: min =         1
                                                               avg =       9.8
                                                               max =        46

OLS estimation
--------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on gwf_leaderid

Number of clusters (gwf_leaderid) = 414               Number of obs =     4038
                                                      F(  4,   413) =   165.94
                                                      Prob > F      =   0.0000
Total (centered) SS     =  256.3043586                Centered R2   =   0.3238
Total (uncentered) SS   =          257                Uncentered R2 =   0.3256
Residual SS             =  173.3246216                Root MSE      =    .2072

------------------------------------------------------------------------------
             |               Robust
    D.xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       lxyrs |
         D1. |   .3157201   .0125081    25.24   0.000     .2912047    .3402354
             |
          lt |
         D1. |   -.109248   .0167575    -6.52   0.000    -.1420921   -.0764039
             |
    lnregion |
         D1. |   .0110373   .0029115     3.79   0.000      .005331    .0167437
             |
       treat |
         D1. |  -.0577685   .0175837    -3.29   0.001    -.0922319   -.0233051
             |
       _cons |   .0156934   .0036726     4.27   0.000     .0084952    .0228915
------------------------------------------------------------------------------
Included instruments: D.lxyrs D.lt D.lnregion D.treat
------------------------------------------------------------------------------

.                         xi:xtivreg2 xonset lxyrs lt lnregion xpers2,fd cluster
> (gwf_leaderid)  

FIRST DIFFERENCES ESTIMATION
----------------------------
Number of groups =       414                    Obs per group: min =         1
                                                               avg =       9.8
                                                               max =        46

OLS estimation
--------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on gwf_leaderid

Number of clusters (gwf_leaderid) = 414               Number of obs =     4038
                                                      F(  4,   413) =   165.78
                                                      Prob > F      =   0.0000
Total (centered) SS     =  256.3043586                Centered R2   =   0.3236
Total (uncentered) SS   =          257                Uncentered R2 =   0.3254
Residual SS             =  173.3674547                Root MSE      =    .2072

------------------------------------------------------------------------------
             |               Robust
    D.xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       lxyrs |
         D1. |    .315771   .0125141    25.23   0.000     .2912438    .3402983
             |
          lt |
         D1. |  -.1094667   .0167456    -6.54   0.000    -.1422876   -.0766459
             |
    lnregion |
         D1. |   .0111344    .002917     3.82   0.000     .0054172    .0168515
             |
      xpers2 |
         D1. |  -.0343345   .0104368    -3.29   0.001    -.0547902   -.0138788
             |
       _cons |   .0159721    .003679     4.34   0.000     .0087614    .0231829
------------------------------------------------------------------------------
Included instruments: D.lxyrs D.lt D.lnregion D.xpers2
------------------------------------------------------------------------------

.                         
.                         * Leader-FE  *
.                         reghdfe xonset lxyrs lt lnregion treat,a(gwf_leaderid 
> year)cluster(gwf_leaderid)
(dropped 89 singleton observations)
(MWFE estimator converged in 13 iterations)

HDFE Linear regression                            Number of obs   =      4,470
Absorbing 2 HDFE groups                           F(   4,    414) =      14.32
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1967
                                                  Adj R-squared   =     0.0993
                                                  Within R-sq.    =     0.0233
Number of clusters (gwf_leaderid) =        415    Root MSE        =     0.1805

                         (Std. err. adjusted for 415 clusters in gwf_leaderid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       lxyrs |   .0442964   .0076601     5.78   0.000     .0292389     .059354
          lt |   .0100534   .0071874     1.40   0.163     -.004075    .0241818
    lnregion |   .0111589   .0039812     2.80   0.005      .003333    .0189847
       treat |  -.0318354   .0127028    -2.51   0.013    -.0568054   -.0068653
       _cons |   .0523131   .0062628     8.35   0.000     .0400023    .0646239
------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------+
  Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------+---------------------------------------|
 gwf_leaderid |       415         415           0    *|
         year |        65           0          65     |
------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.                         reghdfe xonset lxyrs lt lnregion xpers2,a(gwf_leaderid
>  year)cluster(gwf_leaderid)
(dropped 89 singleton observations)
(MWFE estimator converged in 13 iterations)

HDFE Linear regression                            Number of obs   =      4,470
Absorbing 2 HDFE groups                           F(   4,    414) =      14.04
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1960
                                                  Adj R-squared   =     0.0986
                                                  Within R-sq.    =     0.0225
Number of clusters (gwf_leaderid) =        415    Root MSE        =     0.1806

                         (Std. err. adjusted for 415 clusters in gwf_leaderid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       lxyrs |   .0441096   .0076523     5.76   0.000     .0290674    .0591518
          lt |   .0100612   .0072367     1.39   0.165    -.0041641    .0242865
    lnregion |    .011249   .0039876     2.82   0.005     .0034106    .0190874
      xpers2 |  -.0124381   .0082437    -1.51   0.132    -.0286428    .0037666
       _cons |   .0367084    .000233   157.54   0.000     .0362504    .0371665
------------------------------------------------------------------------------

Absorbed degrees of freedom:
------------------------------------------------------+
  Absorbed FE | Categories  - Redundant  = Num. Coefs |
--------------+---------------------------------------|
 gwf_leaderid |       415         415           0    *|
         year |        65           0          65     |
------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.                         
. 
.                 * Base model *
.                 use temp-fe, clear

.                 xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                 probit xonset $cvar xpers2,vce(cluster gwf_caseid)

Iteration 0:   log pseudolikelihood =  -764.5129  
Iteration 1:   log pseudolikelihood = -740.91043  
Iteration 2:   log pseudolikelihood = -740.40707  
Iteration 3:   log pseudolikelihood = -740.40684  
Iteration 4:   log pseudolikelihood = -740.40684  

Probit regression                                       Number of obs =  4,559
                                                        Wald chi2(5)  =  57.31
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -740.40684                       Pseudo R2     = 0.0315

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     coldwar |  -.1942708   .0830035    -2.34   0.019    -.3569547    -.031587
       lxyrs |  -.0558902   .0355797    -1.57   0.116    -.1256251    .0138448
          lt |   .0838423   .0374807     2.24   0.025     .0103814    .1573032
    lnregion |    .132104    .032185     4.10   0.000     .0690225    .1951855
      xpers2 |  -.1198063   .0399513    -3.00   0.003    -.1981094   -.0415032
       _cons |  -1.663042   .0690394   -24.09   0.000    -1.798357   -1.527727
------------------------------------------------------------------------------

.                 margins,dydx(xpers2 lt lnregion)nose

Average marginal effects                                 Number of obs = 4,559

Expression: Pr(xonset), predict()
dy/dx wrt:  lt lnregion xpers2

------------------------------------------------------------------------------
             |      dy/dx
-------------+----------------------------------------------------------------
          lt |   .0070089
    lnregion |   .0110433
      xpers2 |  -.0100153
------------------------------------------------------------------------------

.                 est store b0

.                 xtprobit xonset $cvar xpers2,vce(cluster gwf_caseid)

Fitting comparison model:

Iteration 0:   log pseudolikelihood =  -764.5129  
Iteration 1:   log pseudolikelihood = -740.91043  
Iteration 2:   log pseudolikelihood = -740.40707  
Iteration 3:   log pseudolikelihood = -740.40684  
Iteration 4:   log pseudolikelihood = -740.40684  

Fitting full model:

rho =  0.0     log pseudolikelihood = -740.40684
rho =  0.1     log pseudolikelihood = -737.93392
rho =  0.2     log pseudolikelihood = -743.36799

Iteration 0:   log pseudolikelihood = -737.93385  
Iteration 1:   log pseudolikelihood = -734.13573  
Iteration 2:   log pseudolikelihood = -733.96097  
Iteration 3:   log pseudolikelihood = -733.95029  
Iteration 4:   log pseudolikelihood = -733.95028  

Calculating robust standard errors ...

Random-effects probit regression                     Number of obs    =  4,559
Group variable: gwf_caseid                           Number of groups =    280

Random effects u_i ~ Gaussian                        Obs per group:
                                                                  min =      1
                                                                  avg =   16.3
                                                                  max =     65

Integration method: mvaghermite                      Integration pts. =     12

                                                     Wald chi2(5)     =  48.70
Log pseudolikelihood = -733.95028                    Prob > chi2      = 0.0000

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     coldwar |  -.3034941   .1097552    -2.77   0.006    -.5186104   -.0883778
       lxyrs |   .0519927   .0571514     0.91   0.363     -.060022    .1640075
          lt |   .1062064   .0460377     2.31   0.021     .0159741    .1964387
    lnregion |   .1391665   .0346306     4.02   0.000     .0712917    .2070413
      xpers2 |  -.1550143   .0482532    -3.21   0.001    -.2495889   -.0604397
       _cons |  -1.674404   .0843464   -19.85   0.000     -1.83972   -1.509088
-------------+----------------------------------------------------------------
    /lnsig2u |  -1.642953   .5106928                     -2.643893   -.6420137
-------------+----------------------------------------------------------------
     sigma_u |   .4397818   .1122967                      .2666159    .7254183
         rho |   .1620636   .0693516                      .0663664    .3447915
------------------------------------------------------------------------------

.                 margins,dydx(xpers2 lt lnregion)nose

Average marginal effects                                 Number of obs = 4,559

Expression: Pr(xonset=1), predict(pr)
dy/dx wrt:  lt lnregion xpers2

------------------------------------------------------------------------------
             |      dy/dx
-------------+----------------------------------------------------------------
          lt |    .009318
    lnregion |   .0122098
      xpers2 |  -.0136002
------------------------------------------------------------------------------

.                 est store b1

.                 xtprobit xonset $cvar xpers2 m_coldwar m_lxyrs m_lt m_xpers2 m
> _lnregion,vce(cluster gwf_caseid)

Fitting comparison model:

Iteration 0:   log pseudolikelihood =  -764.5129  
Iteration 1:   log pseudolikelihood = -659.96711  
Iteration 2:   log pseudolikelihood = -648.82173  
Iteration 3:   log pseudolikelihood = -648.73789  
Iteration 4:   log pseudolikelihood = -648.73786  

Fitting full model:

rho =  0.0     log pseudolikelihood = -648.73786
rho =  0.1     log pseudolikelihood = -656.71143

Iteration 0:   log pseudolikelihood = -656.71145  
Iteration 1:   log pseudolikelihood = -650.70802  
Iteration 2:   log pseudolikelihood = -649.31275  
Iteration 3:   log pseudolikelihood = -648.88403  
Iteration 4:   log pseudolikelihood = -648.77626  
Iteration 5:   log pseudolikelihood = -648.74556  
Iteration 6:   log pseudolikelihood = -648.73963  
Iteration 7:   log pseudolikelihood = -648.73828  
Iteration 8:   log pseudolikelihood = -648.73795  
Iteration 9:   log pseudolikelihood = -648.73792  
Iteration 10:  log pseudolikelihood =  -648.7379  

Calculating robust standard errors ...

Random-effects probit regression                     Number of obs    =  4,559
Group variable: gwf_caseid                           Number of groups =    280

Random effects u_i ~ Gaussian                        Obs per group:
                                                                  min =      1
                                                                  avg =   16.3
                                                                  max =     65

Integration method: mvaghermite                      Integration pts. =     12

                                                     Wald chi2(10)    = 185.19
Log pseudolikelihood = -648.7379                     Prob > chi2      = 0.0000

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     coldwar |  -.6139401   .1766527    -3.48   0.001     -.960173   -.2677072
       lxyrs |   .3738142   .0877811     4.26   0.000     .2017664    .5458621
          lt |   .1999492   .0791348     2.53   0.012     .0448478    .3550507
    lnregion |   .1557668   .0372843     4.18   0.000     .0826909    .2288427
      xpers2 |  -.2210705   .0950218    -2.33   0.020    -.4073097   -.0348312
   m_coldwar |   .5122617   .2422633     2.11   0.034     .0374344     .987089
     m_lxyrs |  -.9358366   .1296851    -7.22   0.000    -1.190015   -.6816584
        m_lt |  -.2995528   .1346426    -2.22   0.026    -.5634473   -.0356582
    m_xpers2 |   .2518818   .1119148     2.25   0.024     .0325327    .4712308
  m_lnregion |   -.159728   .1388273    -1.15   0.250    -.4318246    .1123686
       _cons |  -1.969573   .1194546   -16.49   0.000      -2.2037   -1.735447
-------------+----------------------------------------------------------------
    /lnsig2u |  -13.66854   37990.43                     -74473.54     74446.2
-------------+----------------------------------------------------------------
     sigma_u |   .0010763   20.44362                             0           .
         rho |   1.16e-06   .0440049                             0           .
------------------------------------------------------------------------------

.                 margins,dydx(xpers2 lt lnregion)nose

Average marginal effects                                 Number of obs = 4,559

Expression: Pr(xonset=1), predict(pr)
dy/dx wrt:  lt lnregion xpers2

------------------------------------------------------------------------------
             |      dy/dx
-------------+----------------------------------------------------------------
          lt |   .0149391
    lnregion |   .0116381
      xpers2 |  -.0165172
------------------------------------------------------------------------------

.                 est store b2

.                 xtprobit  xonset lxyrs lt lnregion xpers2 m_lxyrs m_lt m_xpers
> 2 m_lnregion y_lxyrs y_lt y_xpers2 y_lnregion,vce(cluster gwf_caseid)

Fitting comparison model:

Iteration 0:   log pseudolikelihood =  -764.5129  
Iteration 1:   log pseudolikelihood = -655.04822  
Iteration 2:   log pseudolikelihood = -641.80454  
Iteration 3:   log pseudolikelihood = -641.54783  
Iteration 4:   log pseudolikelihood = -641.54778  

Fitting full model:

rho =  0.0     log pseudolikelihood = -641.54778
rho =  0.1     log pseudolikelihood = -649.48808

Iteration 0:   log pseudolikelihood = -649.48831  
Iteration 1:   log pseudolikelihood = -643.51679  
Iteration 2:   log pseudolikelihood = -642.11743  
Iteration 3:   log pseudolikelihood = -641.70157  
Iteration 4:   log pseudolikelihood =  -641.5885  
Iteration 5:   log pseudolikelihood = -641.55718  
Iteration 6:   log pseudolikelihood = -641.54968  
Iteration 7:   log pseudolikelihood = -641.54811  
Iteration 8:   log pseudolikelihood = -641.54786  
Iteration 9:   log pseudolikelihood = -641.54784  
Iteration 10:  log pseudolikelihood = -641.54783  

Calculating robust standard errors ...

Random-effects probit regression                     Number of obs    =  4,559
Group variable: gwf_caseid                           Number of groups =    280

Random effects u_i ~ Gaussian                        Obs per group:
                                                                  min =      1
                                                                  avg =   16.3
                                                                  max =     65

Integration method: mvaghermite                      Integration pts. =     12

                                                     Wald chi2(12)    = 214.81
Log pseudolikelihood = -641.54783                    Prob > chi2      = 0.0000

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       lxyrs |   .3213117    .084617     3.80   0.000     .1554654    .4871579
          lt |   .1991166    .076632     2.60   0.009     .0489207    .3493125
    lnregion |   .1635514   .0425921     3.84   0.000     .0800725    .2470304
      xpers2 |  -.2425575   .0997459    -2.43   0.015    -.4380559    -.047059
     m_lxyrs |  -.9439224   .1179793    -8.00   0.000    -1.175158   -.7126872
        m_lt |  -.3284723   .1363462    -2.41   0.016    -.5957061   -.0612386
    m_xpers2 |   .2720298   .1172473     2.32   0.020     .0422293    .5018303
  m_lnregion |  -.4030221   .1229771    -3.28   0.001    -.6440529   -.1619914
     y_lxyrs |    .492858   .2428394     2.03   0.042     .0169016    .9688144
        y_lt |   .2395936   .4591043     0.52   0.602    -.6602344    1.139422
    y_xpers2 |   .2670098   .4904191     0.54   0.586     -.694194    1.228213
  y_lnregion |   .1177956   .0979436     1.20   0.229    -.0741704    .3097616
       _cons |  -2.072286   .0818047   -25.33   0.000    -2.232621   -1.911952
-------------+----------------------------------------------------------------
    /lnsig2u |  -13.69755   47191.62                     -92507.58    92480.18
-------------+----------------------------------------------------------------
     sigma_u |   .0010608   25.02936                             0           .
         rho |   1.13e-06   .0530999                             0           .
------------------------------------------------------------------------------

.                 margins,dydx(xpers2 lt lnregion)nose

Average marginal effects                                 Number of obs = 4,559

Expression: Pr(xonset=1), predict(pr)
dy/dx wrt:  lt lnregion xpers2

------------------------------------------------------------------------------
             |      dy/dx
-------------+----------------------------------------------------------------
          lt |   .0147079
    lnregion |   .0120809
      xpers2 |  -.0179167
------------------------------------------------------------------------------

.                 est store b3

.                 xtprobit xonset lxyrs lt lnregion xpers2 m_lxyrs m_lt m_xpers2
>  m_lnregion y_lxyrs y_lt y_xpers2 y_lnregion mXy_lxyrs mXy_lt mXy_xpers2 mXy_l
> nregion if sample==1,vce(cluster gwf_caseid)

Fitting comparison model:

Iteration 0:   log pseudolikelihood =  -764.5129  
Iteration 1:   log pseudolikelihood = -650.18887  
Iteration 2:   log pseudolikelihood = -637.00834  
Iteration 3:   log pseudolikelihood = -636.79213  
Iteration 4:   log pseudolikelihood = -636.79192  
Iteration 5:   log pseudolikelihood = -636.79192  

Fitting full model:

rho =  0.0     log pseudolikelihood = -636.79192
rho =  0.1     log pseudolikelihood = -644.57721

Iteration 0:   log pseudolikelihood = -644.57738  
Iteration 1:   log pseudolikelihood = -638.71707  
Iteration 2:   log pseudolikelihood = -637.35956  
Iteration 3:   log pseudolikelihood = -636.94585  
Iteration 4:   log pseudolikelihood = -636.83269  
Iteration 5:   log pseudolikelihood = -636.80101  
Iteration 6:   log pseudolikelihood = -636.79385  
Iteration 7:   log pseudolikelihood = -636.79223  
Iteration 8:   log pseudolikelihood = -636.79196  
Iteration 9:   log pseudolikelihood = -636.79196  
Iteration 10:  log pseudolikelihood = -636.79195  

Calculating robust standard errors ...

Random-effects probit regression                     Number of obs    =  4,559
Group variable: gwf_caseid                           Number of groups =    280

Random effects u_i ~ Gaussian                        Obs per group:
                                                                  min =      1
                                                                  avg =   16.3
                                                                  max =     65

Integration method: mvaghermite                      Integration pts. =     12

                                                     Wald chi2(16)    = 209.91
Log pseudolikelihood = -636.79195                    Prob > chi2      = 0.0000

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       lxyrs |   .3367784   .0851286     3.96   0.000     .1699295    .5036273
          lt |   .1941819   .0766926     2.53   0.011     .0438672    .3444966
    lnregion |   .1659432   .0416655     3.98   0.000     .0842803    .2476061
      xpers2 |   -.232663   .0992557    -2.34   0.019    -.4272006   -.0381254
     m_lxyrs |  -.9667648   .1183585    -8.17   0.000    -1.198743   -.7347864
        m_lt |  -.3265345    .141838    -2.30   0.021    -.6045318   -.0485372
    m_xpers2 |   .2353137   .1149656     2.05   0.041     .0099853    .4606421
  m_lnregion |   -.351289   .1189152    -2.95   0.003    -.5843586   -.1182194
     y_lxyrs |   .2538382   .2864606     0.89   0.376    -.3076142    .8152906
        y_lt |   .1901265   .4551411     0.42   0.676    -.7019337    1.082187
    y_xpers2 |   .7230728   .5055367     1.43   0.153     -.267761    1.713907
  y_lnregion |    .126015    .101042     1.25   0.212    -.0720236    .3240536
   mXy_lxyrs |  -.1436198   .2462061    -0.58   0.560    -.6261749    .3389353
      mXy_lt |   -.198441   .3481646    -0.57   0.569    -.8808311    .4839491
  mXy_xpers2 |   .7225748   .3354537     2.15   0.031     .0650977    1.380052
mXy_lnregion |  -.3444908   .1497896    -2.30   0.021     -.638073   -.0509087
       _cons |  -2.048967   .0832893   -24.60   0.000    -2.212211   -1.885723
-------------+----------------------------------------------------------------
    /lnsig2u |  -14.40517   104941.4                     -205695.8      205667
-------------+----------------------------------------------------------------
     sigma_u |   .0007447   39.07271                             0           .
         rho |   5.55e-07   .0581915                             0           .
------------------------------------------------------------------------------

.                 margins,dydx(xpers2 lt lnregion)nose 

Average marginal effects                                 Number of obs = 4,559

Expression: Pr(xonset=1), predict(pr)
dy/dx wrt:  lt lnregion xpers2

------------------------------------------------------------------------------
             |      dy/dx
-------------+----------------------------------------------------------------
          lt |   .0142456
    lnregion |    .012174
      xpers2 |  -.0170687
------------------------------------------------------------------------------

.                 est store b4

.                 
.                 * Check with IRT-2PL pers instead of generalized *
.                 qui sum irtpers if sample==1

.                 replace irtpers=(irtpers-r(mean))/r(sd)
(4,559 real changes made)

.                 sum irtpers

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    irtpers2 |      4,559   -1.26e-09           1  -1.586156   1.831524

.                 qui egen m_irtpers =mean(irtpers2) if sample==1,by(gwf_caseid)

.                 qui egen y_irtpers =mean(irtpers2) if sample==1,by(year)

.                 xtprobit xonset $cvar irtpers2 m_coldwar m_lxyrs m_lt m_irtper
> s m_lnregion,vce(cluster gwf_caseid)

Fitting comparison model:

Iteration 0:   log pseudolikelihood =  -764.5129  
Iteration 1:   log pseudolikelihood = -660.38397  
Iteration 2:   log pseudolikelihood = -649.42269  
Iteration 3:   log pseudolikelihood = -649.34536  
Iteration 4:   log pseudolikelihood = -649.34533  

Fitting full model:

rho =  0.0     log pseudolikelihood = -649.34533
rho =  0.1     log pseudolikelihood = -657.23407

Iteration 0:   log pseudolikelihood = -657.23409  
Iteration 1:   log pseudolikelihood = -651.29482  
Iteration 2:   log pseudolikelihood = -650.01266  
Iteration 3:   log pseudolikelihood =  -649.5346  
Iteration 4:   log pseudolikelihood = -649.38387  
Iteration 5:   log pseudolikelihood = -649.35206  
Iteration 6:   log pseudolikelihood = -649.34617  
Iteration 7:   log pseudolikelihood = -649.34551  
Iteration 8:   log pseudolikelihood = -649.34537  
Iteration 9:   log pseudolikelihood = -649.34537  
Iteration 10:  log pseudolikelihood = -649.34536  

Calculating robust standard errors ...

Random-effects probit regression                     Number of obs    =  4,559
Group variable: gwf_caseid                           Number of groups =    280

Random effects u_i ~ Gaussian                        Obs per group:
                                                                  min =      1
                                                                  avg =   16.3
                                                                  max =     65

Integration method: mvaghermite                      Integration pts. =     12

                                                     Wald chi2(10)    = 188.58
Log pseudolikelihood = -649.34536                    Prob > chi2      = 0.0000

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     coldwar |  -.6164175   .1763964    -3.49   0.000    -.9621481    -.270687
       lxyrs |   .3713123    .087348     4.25   0.000     .2001133    .5425112
          lt |   .1958688   .0787692     2.49   0.013      .041484    .3502537
    lnregion |   .1554589   .0371838     4.18   0.000     .0825799    .2283379
    irtpers2 |  -.2082299   .1006318    -2.07   0.039    -.4054645   -.0109952
   m_coldwar |    .518974    .242417     2.14   0.032     .0438455    .9941026
     m_lxyrs |  -.9308379   .1284823    -7.24   0.000    -1.182658   -.6790173
        m_lt |  -.2939619   .1345054    -2.19   0.029    -.5575876   -.0303361
   m_irtpers |   .2342299   .1143774     2.05   0.041     .0100543    .4584056
  m_lnregion |  -.1583171   .1386946    -1.14   0.254    -.4301536    .1135194
       _cons |   -1.96978   .1200384   -16.41   0.000    -2.205051   -1.734509
-------------+----------------------------------------------------------------
    /lnsig2u |  -14.29842   70146.63                     -137499.2    137470.6
-------------+----------------------------------------------------------------
     sigma_u |   .0007855   27.54951                             0           .
         rho |   6.17e-07   .0432793                             0           .
------------------------------------------------------------------------------

.                 margins,dydx(irtpers)nose

Average marginal effects                                 Number of obs = 4,559

Expression: Pr(xonset=1), predict(pr)
dy/dx wrt:  irtpers2

------------------------------------------------------------------------------
             |      dy/dx
-------------+----------------------------------------------------------------
    irtpers2 |  -.0155713
------------------------------------------------------------------------------

.                 xtprobit xonset lxyrs lt lnregion irtpers2 m_lxyrs m_lt m_irtp
> ers m_lnregion y_lxyrs y_lt y_irtpers y_lnregion,vce(cluster gwf_caseid)

Fitting comparison model:

Iteration 0:   log pseudolikelihood =  -764.5129  
Iteration 1:   log pseudolikelihood = -655.52007  
Iteration 2:   log pseudolikelihood = -642.45574  
Iteration 3:   log pseudolikelihood = -642.20557  
Iteration 4:   log pseudolikelihood = -642.20495  
Iteration 5:   log pseudolikelihood = -642.20495  

Fitting full model:

rho =  0.0     log pseudolikelihood = -642.20495
rho =  0.1     log pseudolikelihood = -650.03952

Iteration 0:   log pseudolikelihood = -650.03976  
Iteration 1:   log pseudolikelihood = -644.15441  
Iteration 2:   log pseudolikelihood = -642.77011  
Iteration 3:   log pseudolikelihood = -642.35719  
Iteration 4:   log pseudolikelihood = -642.24538  
Iteration 5:   log pseudolikelihood =  -642.2146  
Iteration 6:   log pseudolikelihood = -642.20683  
Iteration 7:   log pseudolikelihood = -642.20528  
Iteration 8:   log pseudolikelihood = -642.20503  
Iteration 9:   log pseudolikelihood = -642.20501  
Iteration 10:  log pseudolikelihood =   -642.205  

Calculating robust standard errors ...

Random-effects probit regression                     Number of obs    =  4,559
Group variable: gwf_caseid                           Number of groups =    280

Random effects u_i ~ Gaussian                        Obs per group:
                                                                  min =      1
                                                                  avg =   16.3
                                                                  max =     65

Integration method: mvaghermite                      Integration pts. =     12

                                                     Wald chi2(12)    = 221.35
Log pseudolikelihood = -642.205                      Prob > chi2      = 0.0000

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       lxyrs |   .3188343   .0841654     3.79   0.000     .1538732    .4837955
          lt |   .1947716   .0761429     2.56   0.011     .0455343    .3440089
    lnregion |   .1644659   .0426182     3.86   0.000     .0809357    .2479962
    irtpers2 |  -.2318391    .104767    -2.21   0.027    -.4371786   -.0264996
     m_lxyrs |  -.9388709   .1170633    -8.02   0.000    -1.168311   -.7094312
        m_lt |  -.3228587   .1363593    -2.37   0.018    -.5901181   -.0555994
   m_irtpers |   .2569482   .1197223     2.15   0.032     .0222968    .4915995
  m_lnregion |  -.4088471   .1238233    -3.30   0.001    -.6515363   -.1661578
     y_lxyrs |    .500956    .245421     2.04   0.041     .0199396    .9819723
        y_lt |   .2466377   .4742766     0.52   0.603    -.6829274    1.176203
   y_irtpers |   .2252555    .493042     0.46   0.648     -.741089      1.1916
  y_lnregion |   .1149562   .0983595     1.17   0.243    -.0778249    .3077372
       _cons |  -2.069422   .0811572   -25.50   0.000    -2.228487   -1.910356
-------------+----------------------------------------------------------------
    /lnsig2u |  -13.68346   46112.94                     -90393.38    90366.02
-------------+----------------------------------------------------------------
     sigma_u |   .0010683   24.63011                             0           .
         rho |   1.14e-06   .0526222                             0           .
------------------------------------------------------------------------------

.                 margins,dydx(irtpers)nose

Average marginal effects                                 Number of obs = 4,559

Expression: Pr(xonset=1), predict(pr)
dy/dx wrt:  irtpers2

------------------------------------------------------------------------------
             |      dy/dx
-------------+----------------------------------------------------------------
    irtpers2 |  -.0171426
------------------------------------------------------------------------------

. 
.                 
.                 * Various measures of personalism *
.                 xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                 probit xonset $cvar gwf_pers,vce(cluster gwf_caseid)

Iteration 0:   log pseudolikelihood =  -764.5129  
Iteration 1:   log pseudolikelihood =  -745.5319  
Iteration 2:   log pseudolikelihood = -745.16801  
Iteration 3:   log pseudolikelihood =  -745.1679  
Iteration 4:   log pseudolikelihood =  -745.1679  

Probit regression                                       Number of obs =  4,559
                                                        Wald chi2(5)  =  45.33
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -745.1679                        Pseudo R2     = 0.0253

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     coldwar |  -.1752538   .0814259    -2.15   0.031    -.3348457   -.0156619
       lxyrs |  -.0709143   .0347668    -2.04   0.041     -.139056   -.0027727
          lt |   .0468011   .0346852     1.35   0.177    -.0211807    .1147829
    lnregion |   .1277541   .0320869     3.98   0.000      .064865    .1906432
gwf_personal |    .069464   .0828152     0.84   0.402    -.0928507    .2317787
       _cons |  -1.682587   .0710027   -23.70   0.000     -1.82175   -1.543425
------------------------------------------------------------------------------

.                 est store p1

.                 probit xonset $cvar xpers,vce(cluster gwf_caseid)

Iteration 0:   log pseudolikelihood =  -764.5129  
Iteration 1:   log pseudolikelihood = -741.96977  
Iteration 2:   log pseudolikelihood = -741.49416  
Iteration 3:   log pseudolikelihood = -741.49397  
Iteration 4:   log pseudolikelihood = -741.49397  

Probit regression                                       Number of obs =  4,559
                                                        Wald chi2(5)  =  52.35
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -741.49397                       Pseudo R2     = 0.0301

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     coldwar |  -.2010733   .0827588    -2.43   0.015    -.3632776    -.038869
       lxyrs |  -.0647247   .0354965    -1.82   0.068    -.1342966    .0048473
          lt |   .0948753   .0399493     2.37   0.018     .0165761    .1731745
    lnregion |    .131413   .0321029     4.09   0.000     .0684925    .1943336
       xpers |  -.1117674   .0453094    -2.47   0.014    -.2005722   -.0229625
       _cons |  -1.655995   .0690733   -23.97   0.000    -1.791376   -1.520614
------------------------------------------------------------------------------

.                 margins,dydx(xpers lt)

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(xonset), predict()
dy/dx wrt:  lt xpers

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
          lt |   .0079409   .0033713     2.36   0.019     .0013333    .0145485
       xpers |  -.0093547   .0039219    -2.39   0.017    -.0170415   -.0016679
------------------------------------------------------------------------------

.                 est store p2

.                 xtprobit xonset  $cvar xpers,vce(cluster gwf_caseid)

Fitting comparison model:

Iteration 0:   log pseudolikelihood =  -764.5129  
Iteration 1:   log pseudolikelihood = -741.96977  
Iteration 2:   log pseudolikelihood = -741.49416  
Iteration 3:   log pseudolikelihood = -741.49397  
Iteration 4:   log pseudolikelihood = -741.49397  

Fitting full model:

rho =  0.0     log pseudolikelihood = -741.49397
rho =  0.1     log pseudolikelihood = -739.10892
rho =  0.2     log pseudolikelihood = -744.62095

Iteration 0:   log pseudolikelihood =  -739.1091  
Iteration 1:   log pseudolikelihood =  -735.0856  
Iteration 2:   log pseudolikelihood = -734.86913  
Iteration 3:   log pseudolikelihood = -734.85176  
Iteration 4:   log pseudolikelihood = -734.85173  

Calculating robust standard errors ...

Random-effects probit regression                     Number of obs    =  4,559
Group variable: gwf_caseid                           Number of groups =    280

Random effects u_i ~ Gaussian                        Obs per group:
                                                                  min =      1
                                                                  avg =   16.3
                                                                  max =     65

Integration method: mvaghermite                      Integration pts. =     12

                                                     Wald chi2(5)     =  44.19
Log pseudolikelihood = -734.85173                    Prob > chi2      = 0.0000

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     coldwar |   -.317051    .112542    -2.82   0.005    -.5376293   -.0964727
       lxyrs |   .0503134    .060705     0.83   0.407    -.0686662    .1692931
          lt |   .1179751   .0484013     2.44   0.015     .0231103    .2128398
    lnregion |   .1383719   .0345954     4.00   0.000     .0705661    .2061777
       xpers |  -.1465264   .0526244    -2.78   0.005    -.2496684   -.0433844
       _cons |   -1.66929   .0852732   -19.58   0.000    -1.836422   -1.502157
-------------+----------------------------------------------------------------
    /lnsig2u |   -1.58905   .5344901                     -2.636631   -.5414685
-------------+----------------------------------------------------------------
     sigma_u |   .4517958   .1207402                      .2675856    .7628192
         rho |   .1695176   .0752463                      .0668178     .367846
------------------------------------------------------------------------------

.                 margins,dydx(xpers lt)

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(xonset=1), predict(pr)
dy/dx wrt:  lt xpers

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
          lt |    .010348   .0043855     2.36   0.018     .0017527    .0189434
       xpers |  -.0128524   .0048368    -2.66   0.008    -.0223324   -.0033724
------------------------------------------------------------------------------

.                 est store p3

.                 xtprobit xonset $cvar xpers1,vce(cluster gwf_caseid)

Fitting comparison model:

Iteration 0:   log pseudolikelihood =  -764.5129  
Iteration 1:   log pseudolikelihood = -745.82122  
Iteration 2:   log pseudolikelihood = -745.45479  
Iteration 3:   log pseudolikelihood = -745.45467  
Iteration 4:   log pseudolikelihood = -745.45467  

Fitting full model:

rho =  0.0     log pseudolikelihood = -745.45467
rho =  0.1     log pseudolikelihood = -743.30177
rho =  0.2     log pseudolikelihood = -749.12105

Iteration 0:   log pseudolikelihood = -743.30147  
Iteration 1:   log pseudolikelihood = -738.96752  
Iteration 2:   log pseudolikelihood = -738.69624  
Iteration 3:   log pseudolikelihood = -738.65914  
Iteration 4:   log pseudolikelihood =   -738.659  
Iteration 5:   log pseudolikelihood =   -738.659  

Calculating robust standard errors ...

Random-effects probit regression                     Number of obs    =  4,559
Group variable: gwf_caseid                           Number of groups =    280

Random effects u_i ~ Gaussian                        Obs per group:
                                                                  min =      1
                                                                  avg =   16.3
                                                                  max =     65

Integration method: mvaghermite                      Integration pts. =     12

                                                     Wald chi2(5)     =  39.37
Log pseudolikelihood = -738.659                      Prob > chi2      = 0.0000

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     coldwar |   -.309965   .1140052    -2.72   0.007     -.533411   -.0865189
       lxyrs |   .0453755   .0615248     0.74   0.461    -.0752109     .165962
          lt |   .0770208   .0484016     1.59   0.112    -.0178446    .1718862
    lnregion |   .1372217   .0344536     3.98   0.000     .0696938    .2047495
      xpers1 |  -.0470085   .0515943    -0.91   0.362    -.1481315    .0541145
       _cons |  -1.672709    .084694   -19.75   0.000    -1.838707   -1.506712
-------------+----------------------------------------------------------------
    /lnsig2u |  -1.548907   .5399928                     -2.607273   -.4905406
-------------+----------------------------------------------------------------
     sigma_u |   .4609556   .1244564                      .2715425     .782493
         rho |   .1752442   .0780471                      .0686718    .3797662
------------------------------------------------------------------------------

.                 margins,dydx(xpers1 lt)

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(xonset=1), predict(pr)
dy/dx wrt:  lt xpers1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
          lt |   .0067523   .0043455     1.55   0.120    -.0017648    .0152694
      xpers1 |  -.0041212   .0046111    -0.89   0.371    -.0131587    .0049163
------------------------------------------------------------------------------

.                 est store p4    

.                 xtprobit xonset $cvar xpers2,vce(cluster gwf_caseid)

Fitting comparison model:

Iteration 0:   log pseudolikelihood =  -764.5129  
Iteration 1:   log pseudolikelihood = -740.91043  
Iteration 2:   log pseudolikelihood = -740.40707  
Iteration 3:   log pseudolikelihood = -740.40684  
Iteration 4:   log pseudolikelihood = -740.40684  

Fitting full model:

rho =  0.0     log pseudolikelihood = -740.40684
rho =  0.1     log pseudolikelihood = -737.93392
rho =  0.2     log pseudolikelihood = -743.36799

Iteration 0:   log pseudolikelihood = -737.93385  
Iteration 1:   log pseudolikelihood = -734.13573  
Iteration 2:   log pseudolikelihood = -733.96097  
Iteration 3:   log pseudolikelihood = -733.95029  
Iteration 4:   log pseudolikelihood = -733.95028  

Calculating robust standard errors ...

Random-effects probit regression                     Number of obs    =  4,559
Group variable: gwf_caseid                           Number of groups =    280

Random effects u_i ~ Gaussian                        Obs per group:
                                                                  min =      1
                                                                  avg =   16.3
                                                                  max =     65

Integration method: mvaghermite                      Integration pts. =     12

                                                     Wald chi2(5)     =  48.70
Log pseudolikelihood = -733.95028                    Prob > chi2      = 0.0000

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     coldwar |  -.3034941   .1097552    -2.77   0.006    -.5186104   -.0883778
       lxyrs |   .0519927   .0571514     0.91   0.363     -.060022    .1640075
          lt |   .1062064   .0460377     2.31   0.021     .0159741    .1964387
    lnregion |   .1391665   .0346306     4.02   0.000     .0712917    .2070413
      xpers2 |  -.1550143   .0482532    -3.21   0.001    -.2495889   -.0604397
       _cons |  -1.674404   .0843464   -19.85   0.000     -1.83972   -1.509088
-------------+----------------------------------------------------------------
    /lnsig2u |  -1.642953   .5106928                     -2.643893   -.6420137
-------------+----------------------------------------------------------------
     sigma_u |   .4397818   .1122967                      .2666159    .7254183
         rho |   .1620636   .0693516                      .0663664    .3447915
------------------------------------------------------------------------------

.                 margins,dydx(xpers2 lt lnregion)

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(xonset=1), predict(pr)
dy/dx wrt:  lt lnregion xpers2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
          lt |    .009318   .0041679     2.24   0.025      .001149     .017487
    lnregion |   .0122098    .003106     3.93   0.000     .0061222    .0182974
      xpers2 |  -.0136002   .0044733    -3.04   0.002    -.0223677   -.0048326
------------------------------------------------------------------------------

.                 est store p5                    

.                 xtprobit xonset $cvar xpers1 xpers2,vce(cluster gwf_caseid)

Fitting comparison model:

Iteration 0:   log pseudolikelihood =  -764.5129  
Iteration 1:   log pseudolikelihood = -740.88686  
Iteration 2:   log pseudolikelihood = -740.38247  
Iteration 3:   log pseudolikelihood = -740.38223  
Iteration 4:   log pseudolikelihood = -740.38223  

Fitting full model:

rho =  0.0     log pseudolikelihood = -740.38223
rho =  0.1     log pseudolikelihood =  -737.9594
rho =  0.2     log pseudolikelihood =  -743.4174

Iteration 0:   log pseudolikelihood = -737.95932  
Iteration 1:   log pseudolikelihood = -734.13007  
Iteration 2:   log pseudolikelihood = -733.94671  
Iteration 3:   log pseudolikelihood = -733.93435  
Iteration 4:   log pseudolikelihood = -733.93433  

Calculating robust standard errors ...

Random-effects probit regression                     Number of obs    =  4,559
Group variable: gwf_caseid                           Number of groups =    280

Random effects u_i ~ Gaussian                        Obs per group:
                                                                  min =      1
                                                                  avg =   16.3
                                                                  max =     65

Integration method: mvaghermite                      Integration pts. =     12

                                                     Wald chi2(6)     =  49.18
Log pseudolikelihood = -733.93433                    Prob > chi2      = 0.0000

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     coldwar |  -.3042284   .1101288    -2.76   0.006    -.5200769   -.0883798
       lxyrs |   .0528172   .0577197     0.92   0.360    -.0603113    .1659457
          lt |   .1085538   .0500614     2.17   0.030     .0104353    .2066724
    lnregion |   .1392214   .0346763     4.01   0.000     .0712571    .2071858
      xpers1 |  -.0087372   .0530567    -0.16   0.869    -.1127264     .095252
      xpers2 |  -.1530296    .049512    -3.09   0.002    -.2500714   -.0559879
       _cons |   -1.67445   .0845059   -19.81   0.000    -1.840079   -1.508822
-------------+----------------------------------------------------------------
    /lnsig2u |  -1.632905   .5237585                     -2.659452   -.6063567
-------------+----------------------------------------------------------------
     sigma_u |    .441997   .1157498                      .2645497    .7384674
         rho |   .1634329   .0716096                      .0654088    .3528907
------------------------------------------------------------------------------

.                 est store p6

.                 xtprobit xonset $cvar gwf_pers,vce(cluster gwf_caseid)

Fitting comparison model:

Iteration 0:   log pseudolikelihood =  -764.5129  
Iteration 1:   log pseudolikelihood =  -745.5319  
Iteration 2:   log pseudolikelihood = -745.16801  
Iteration 3:   log pseudolikelihood =  -745.1679  
Iteration 4:   log pseudolikelihood =  -745.1679  

Fitting full model:

rho =  0.0     log pseudolikelihood =  -745.1679
rho =  0.1     log pseudolikelihood = -743.29746
rho =  0.2     log pseudolikelihood = -749.34793

Iteration 0:   log pseudolikelihood = -743.29708  
Iteration 1:   log pseudolikelihood = -739.07355  
Iteration 2:   log pseudolikelihood = -738.83294  
Iteration 3:   log pseudolikelihood = -738.79409  
Iteration 4:   log pseudolikelihood = -738.79391  
Iteration 5:   log pseudolikelihood = -738.79391  

Calculating robust standard errors ...

Random-effects probit regression                     Number of obs    =  4,559
Group variable: gwf_caseid                           Number of groups =    280

Random effects u_i ~ Gaussian                        Obs per group:
                                                                  min =      1
                                                                  avg =   16.3
                                                                  max =     65

Integration method: mvaghermite                      Integration pts. =     12

                                                     Wald chi2(5)     =  40.27
Log pseudolikelihood = -738.79391                    Prob > chi2      = 0.0000

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     coldwar |  -.2941518    .112375    -2.62   0.009    -.5144028   -.0739008
       lxyrs |   .0394517   .0594089     0.66   0.507    -.0769876     .155891
          lt |   .0603391   .0420789     1.43   0.152    -.0221341    .1428123
    lnregion |   .1358327   .0343663     3.95   0.000      .068476    .2031893
gwf_personal |   .0909152   .1103315     0.82   0.410    -.1253305    .3071609
       _cons |  -1.706303   .0917073   -18.61   0.000    -1.886046    -1.52656
-------------+----------------------------------------------------------------
    /lnsig2u |  -1.623034   .5282622                     -2.658409   -.5876594
-------------+----------------------------------------------------------------
     sigma_u |   .4441836   .1173227                      .2646877    .7454034
         rho |   .1647868   .0727059                      .0654726    .3571721
------------------------------------------------------------------------------

.                 
.                 * Random intercept *
.                 xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                 melogit xonset $cvar xpers2||gwf_caseid:,vce(cluster gwf_casei
> d)        

Fitting fixed-effects model:

Iteration 0:   log likelihood = -861.94402  
Iteration 1:   log likelihood = -743.56845  
Iteration 2:   log likelihood = -741.00091  
Iteration 3:   log likelihood = -740.99307  
Iteration 4:   log likelihood = -740.99307  

Refining starting values:

Grid node 0:   log likelihood = -743.25904

Fitting full model:

Iteration 0:   log pseudolikelihood = -743.25904  (not concave)
Iteration 1:   log pseudolikelihood = -737.69566  
Iteration 2:   log pseudolikelihood = -734.51706  
Iteration 3:   log pseudolikelihood = -734.42075  
Iteration 4:   log pseudolikelihood = -734.42061  
Iteration 5:   log pseudolikelihood = -734.42061  

Mixed-effects logistic regression               Number of obs     =      4,559
Group variable: gwf_caseid                      Number of groups  =        280

                                                Obs per group:
                                                              min =          1
                                                              avg =       16.3
                                                              max =         65

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(5)      =      51.23
Log pseudolikelihood = -734.42061               Prob > chi2       =     0.0000
                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     coldwar |  -.6571152   .2451805    -2.68   0.007     -1.13766   -.1765701
       lxyrs |   .1218931   .1292254     0.94   0.346    -.1313841    .3751702
          lt |    .212962   .1008786     2.11   0.035     .0152437    .4106804
    lnregion |   .2895649   .0726973     3.98   0.000     .1470808    .4320489
      xpers2 |  -.3335985   .1031393    -3.23   0.001    -.5357478   -.1314492
       _cons |  -3.058581   .1847505   -16.56   0.000    -3.420685   -2.696477
-------------+----------------------------------------------------------------
gwf_caseid   |
   var(_cons)|   .8717596   .4625005                       .308179    2.465985
------------------------------------------------------------------------------

.                 * Random slope *
.                 melogit xonset $cvar xpers2||gwf_caseid:xpers2,vce(cluster gwf
> _caseid)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -861.94402  
Iteration 1:   log likelihood = -743.56845  
Iteration 2:   log likelihood = -741.00091  
Iteration 3:   log likelihood = -740.99307  
Iteration 4:   log likelihood = -740.99307  

Refining starting values:

Grid node 0:   log likelihood = -754.71054

Fitting full model:

Iteration 0:   log pseudolikelihood = -754.71054  (not concave)
Iteration 1:   log pseudolikelihood = -746.72802  (not concave)
Iteration 2:   log pseudolikelihood = -742.91955  (not concave)
Iteration 3:   log pseudolikelihood = -738.16888  
Iteration 4:   log pseudolikelihood = -734.38951  
Iteration 5:   log pseudolikelihood = -734.30855  
Iteration 6:   log pseudolikelihood =  -734.3082  
Iteration 7:   log pseudolikelihood =  -734.3082  

Mixed-effects logistic regression               Number of obs     =      4,559
Group variable: gwf_caseid                      Number of groups  =        280

                                                Obs per group:
                                                              min =          1
                                                              avg =       16.3
                                                              max =         65

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(5)      =      46.95
Log pseudolikelihood = -734.3082                Prob > chi2       =     0.0000
                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     coldwar |   -.667821    .258062    -2.59   0.010    -1.173613   -.1620288
       lxyrs |   .1337491   .1399525     0.96   0.339    -.1405528     .408051
          lt |   .2207974    .100137     2.20   0.027     .0245325    .4170624
    lnregion |   .2917098   .0729847     4.00   0.000     .1486624    .4347572
      xpers2 |  -.3390743   .1086169    -3.12   0.002    -.5519595   -.1261892
       _cons |  -3.067911   .1849792   -16.59   0.000    -3.430463   -2.705358
-------------+----------------------------------------------------------------
gwf_caseid   |
  var(xpers2)|    .091842   .2252711                      .0007502    11.24289
   var(_cons)|   .8618392   .4952437                      .2794444    2.658013
------------------------------------------------------------------------------

.                 melogit xonset $cvar xpers2||gwf_caseid:lt lxyrs,vce(cluster g
> wf_caseid)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -861.94402  
Iteration 1:   log likelihood = -743.56845  
Iteration 2:   log likelihood = -741.00091  
Iteration 3:   log likelihood = -740.99307  
Iteration 4:   log likelihood = -740.99307  

Refining starting values:

Grid node 0:   log likelihood = -770.10621

Fitting full model:

Iteration 0:   log pseudolikelihood = -770.10621  (not concave)
Iteration 1:   log pseudolikelihood = -755.07823  (not concave)
Iteration 2:   log pseudolikelihood = -747.64862  (not concave)
Iteration 3:   log pseudolikelihood = -738.40212  
Iteration 4:   log pseudolikelihood = -733.89284  
Iteration 5:   log pseudolikelihood = -733.49289  
Iteration 6:   log pseudolikelihood = -733.48609  
Iteration 7:   log pseudolikelihood = -733.48615  

Mixed-effects logistic regression               Number of obs     =      4,559
Group variable: gwf_caseid                      Number of groups  =        280

                                                Obs per group:
                                                              min =          1
                                                              avg =       16.3
                                                              max =         65

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(5)      =      43.05
Log pseudolikelihood = -733.48615               Prob > chi2       =     0.0000
                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     coldwar |  -.7009457   .2645288    -2.65   0.008    -1.219413   -.1824789
       lxyrs |   .2013349   .1421306     1.42   0.157    -.0772359    .4799058
          lt |   .2809786   .1374315     2.04   0.041     .0116178    .5503394
    lnregion |   .2934974   .0739775     3.97   0.000     .1485042    .4384906
      xpers2 |  -.3689835   .1233833    -2.99   0.003    -.6108104   -.1271567
       _cons |  -3.125613    .216614   -14.43   0.000    -3.550169   -2.701058
-------------+----------------------------------------------------------------
gwf_caseid   |
      var(lt)|   .1153463   .1464978                      .0095701    1.390236
   var(lxyrs)|   .1341797   .1441785                      .0163326    1.102347
   var(_cons)|   1.098269   .6055867                      .3726965      3.2364
------------------------------------------------------------------------------

.                 
.                 * Drop controls
.                  xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                  qui xtprobit xonset lxyrs coldwar lt lnregion xpers2,vce(clus
> ter gwf_caseid)   

.                  est store c1

.                  qui xtprobit xonset lxyrs xpers2,vce(cluster gwf_caseid)

.                  lincom xpers2 

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0951802   .0400217    -2.38   0.017    -.1736213   -.0167391
------------------------------------------------------------------------------

.                  est store c2

.                  qui xtprobit xonset lxyrs lt xpers2,vce(cluster gwf_caseid)

.                  lincom xpers2 

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1366822   .0450838    -3.03   0.002    -.2250448   -.0483195
------------------------------------------------------------------------------

.                  est store c3

.                  qui xtprobit xonset lxyrs lnregion xpers2,vce(cluster gwf_cas
> eid)

.                  lincom xpers2           

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1056874     .04151    -2.55   0.011    -.1870456   -.0243292
------------------------------------------------------------------------------

.                  est store c4

.                  qui xtprobit xonset lxyrs coldwar xpers2,vce(cluster gwf_case
> id)

.                  lincom xpers2

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1119256   .0430884    -2.60   0.009    -.1963772   -.0274739
------------------------------------------------------------------------------

.                  est store c5

.                  qui xtprobit xonset lxyrs coldwar lnregion xpers2,vce(cluster
>  gwf_caseid)

.                  lincom xpers2

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1162128   .0437383    -2.66   0.008    -.2019383   -.0304874
------------------------------------------------------------------------------

.                  est store c6

.                  qui xtprobit xonset lxyrs coldwar lt lnregion xpers2,vce(clus
> ter gwf_caseid)

.                  lincom xpers2

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1550143   .0482532    -3.21   0.001    -.2495889   -.0604397
------------------------------------------------------------------------------

.                  est store c7

.                  qui xtprobit xonset lxyrs lt lnregion xpers2,vce(cluster gwf_
> caseid)

.                  lincom xpers2

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1450864   .0465316    -3.12   0.002    -.2362866   -.0538862
------------------------------------------------------------------------------

.                  est store c8   

.                  qui xtprobit xonset lxyrs coldwar xpers2,vce(cluster gwf_case
> id)

.                  lincom xpers2

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1119256   .0430884    -2.60   0.009    -.1963772   -.0274739
------------------------------------------------------------------------------

.                  est store c9

.                 * No controls estimates table *
.                 estout c1 c2 c3 c4 c5 c6 c7 c8 c9 using TableB2.tex, cells(b(s
> tar  fmt(%9.3f)) se(par fmt(%9.2f))) ///
>                         stats(r2 N N_clust) style(tex) replace label starlevel
> s(* 0.05) title(\label{tabB2}) 
(file TableB2.tex not found)
(output written to TableB2.tex)

.                  
.                 **** Adding covariates, one at a time ****
.                 use temp-fe,clear

.                 spearman xpers2 v2x_clpol v2clkill v2cltort v2juhcind v2x_juco
> n v2x_frassoc_thick v2x_freexp_altinf v2x_partipdem v2x_libdem v2x_polyarchy
(obs=4554)

             |   xpers2 v2x_cl~l v2clkill v2cltort v2juhc~d v2x_ju~n v2x_fr~k
-------------+---------------------------------------------------------------
      xpers2 |   1.0000 
   v2x_clpol |  -0.3236   1.0000 
    v2clkill |  -0.3969   0.5047   1.0000 
    v2cltort |  -0.3467   0.5513   0.7997   1.0000 
   v2juhcind |  -0.2470   0.4358   0.2038   0.2687   1.0000 
   v2x_jucon |  -0.3535   0.4839   0.3541   0.4297   0.8111   1.0000 
v2x_frasso~k |  -0.2836   0.9217   0.3856   0.4358   0.4053   0.4333   1.0000 
v2x_freexp~f |  -0.2690   0.9501   0.4272   0.4882   0.4123   0.4496   0.8471 
v2x_partip~m |  -0.2507   0.7686   0.3765   0.3985   0.3936   0.4311   0.7646 
  v2x_libdem |  -0.3497   0.8164   0.5578   0.6016   0.6044   0.7278   0.7574 
v2x_polyar~y |  -0.2500   0.7712   0.3872   0.4363   0.3142   0.3712   0.7900 

             | v2x_fr~f v2x_pa~m v2x_li~m v2x_po~y
-------------+------------------------------------
v2x_freexp~f |   1.0000 
v2x_partip~m |   0.7472   1.0000 
  v2x_libdem |   0.7613   0.7417   1.0000 
v2x_polyar~y |   0.7218   0.8180   0.7856   1.0000 

.                 gen n=_n

.                 gen beta=.
(4,559 missing values generated)

.                 gen hi=.
(4,559 missing values generated)

.                 gen lo=.
(4,559 missing values generated)

.                 gen hi90=.
(4,559 missing values generated)

.                 gen lo90=.
(4,559 missing values generated)

.                 gen varname=""
(4,559 missing values generated)

.                 xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                 global cvar ="coldwar lxyrs lt lnregion"

.                 global varA= "lag_xongoing loggdp logoil support gwf_mil leade
> rmil seizure_coup coup12 election ythbul4 wdipopurbmi wditrade"

.                 global varB= "polparcomp polpolcomp polexconst v2x_polyarchy v
> 2x_libdem v2x_partipdem v2x_freexp_altinf v2x_frassoc_thick "

.                 global varC= "v2x_clpol v2x_jucon v2juhcind e_v2x_neopat prior
> dem legcomp excluded monoethnic  multiethnic civwar grow"

.                 global varD= "v2cltort v2clkill lag_repress leadermil milethni
> c_homo nmc_logmilper nmc_logmilex effectivenumber debruin_cbcount debruin_ha_c
> bcount lpop"

.                 local var = "$varA $varB $varC $varD"

.                 local i =1

.                 foreach v of local var {
  2.                         di "`v'"
  3.                         qui xtset gwf_caseid
  4.                         qui xtprobit xonset $cvar `v' xpers2,vce(cluster gw
> f_caseid)     
  5.                         qui nlcom _b[xpers2],post
  6.                         matrix beta =e(b)  
  7.                         local b = beta[1,1]
  8.                         qui replace beta=`b' if n==`i'
  9.                         matrix var = e(V) 
 10.                         local se =var[1,1]
 11.                         qui replace hi = `b' + sqrt(`se')*1.96 if n==`i'
 12.                         qui replace lo = `b' - sqrt(`se')*1.96 if n==`i'
 13.                         qui replace hi90 = `b' + sqrt(`se')*1.65 if n==`i'
 14.                         qui replace lo90 = `b' - sqrt(`se')*1.65 if n==`i'
 15.                         qui replace varname = "`v'" if n==`i'
 16.                         local i = `i' +1
 17.                 }
lag_xongoing
loggdp
logoil
support
gwf_mil
leadermil
seizure_coup
coup12
election
ythbul4
wdipopurbmi
wditrade
polparcomp
polpolcomp
polexconst
v2x_polyarchy
v2x_libdem
v2x_partipdem
v2x_freexp_altinf
v2x_frassoc_thick
v2x_clpol
v2x_jucon
v2juhcind
e_v2x_neopat
priordem
legcomp
excluded
monoethnic
multiethnic
civwar
grow
v2cltort
v2clkill
lag_repress
leadermil
milethnic_homo
nmc_logmilper
nmc_logmilex
effectivenumber
debruin_cbcount
debruin_ha_cbcount
lpop

.                 label define varlab 1 "Ongoing protest campaign" 2 "GDP per ca
> pita (log)" 3 "Oil per capita (log)"  ///
>                         4 "Support party" 5 "Military junta" 6 "Military leade
> r" 7 "Coup seizure" 8 "Recent coup" 9 "Election" ///
>                         10 "Youth bulge" 11 "Urban pop." 12 "Trade"   13 "Parc
> omp" 14 "Polcomp" 15 "Exec. constr." ///
>                         16 "V-Polyarchy" 17 "V-Liberal" 18 "V-Participation" 1
> 9 "V-Free express. info" ///
>                         20 "V-Free express. org" 21 "V-Civil lib." ///
>                         22 "V-Judical constr." 23 "V-Judicial indep." 24 "V-Ne
> opatrimonialism" 25 "Prior democracy" ///
>                         26 "Leg. comp." 27 "Excluded pop" ///
>                         28 "Monoethnic party" 29 "Multiethnic party" 30 "Civil
>  war" 31 "Growth"  32 "V-Torture" ///
>                         33 "V-Kill" 34 "Repression" 35 "Military leader" ///
>                         36 "Ethnic homo military" 37 "Military personnel (log)
> " 38 "Military exp. (log)" ///
>                         39 "Effective number"  40 "Counter-weights" 41 "Heavil
> y armed cw" 42 "Population",replace

.                 label values n varlab

.                 twoway (scatter beta n if n<=42,mcol(blue)yline(-.1275468,lcol
> (red)lpat(dash_dot))) ///
>                         (rspike hi lo n if n<=42,lw(vthin)lcol(blue)) ///
>                         (rspike hi90 lo90 n if n<=42,lcol(blue)lw(medium)ytitl
> e("{&beta}{sub:Security personalization}", ///
>                         size(large)height(-2)) ///
>                         xtitle(Added covariate)yline(0,lpat(dash)lcol(gs6))xla
> b(1(1)42,valuelabel angle(90))legend(off))

.                 graph export "$dir\baseline-covariates.pdf", as(pdf)   replace
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\baseline-covariates.pdf saved as PDF format

. 
.                 * Calendar time trends *
.                 xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                 xtprobit xonset lxyrs lt lnregion xpers2 if year<1989,vce(clus
> ter gwf_caseid)   

Fitting comparison model:

Iteration 0:   log pseudolikelihood = -403.29763  
Iteration 1:   log pseudolikelihood = -397.17233  
Iteration 2:   log pseudolikelihood = -397.09005  
Iteration 3:   log pseudolikelihood = -397.09003  

Fitting full model:

rho =  0.0     log pseudolikelihood = -397.09003
rho =  0.1     log pseudolikelihood = -396.01777
rho =  0.2     log pseudolikelihood = -399.67665

Iteration 0:   log pseudolikelihood = -396.01764  
Iteration 1:   log pseudolikelihood = -394.08053  
Iteration 2:   log pseudolikelihood =  -394.0026  
Iteration 3:   log pseudolikelihood =  -393.9936  
Iteration 4:   log pseudolikelihood = -393.99357  

Calculating robust standard errors ...

Random-effects probit regression                     Number of obs    =  3,086
Group variable: gwf_caseid                           Number of groups =    226

Random effects u_i ~ Gaussian                        Obs per group:
                                                                  min =      1
                                                                  avg =   13.7
                                                                  max =     43

Integration method: mvaghermite                      Integration pts. =     12

                                                     Wald chi2(4)     =  12.10
Log pseudolikelihood = -393.99357                    Prob > chi2      = 0.0166

                           (Std. err. adjusted for 226 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       lxyrs |   .0543515   .0681133     0.80   0.425     -.079148    .1878511
          lt |   .0749282   .0609641     1.23   0.219    -.0445593    .1944156
    lnregion |   .1187835   .0611055     1.94   0.052    -.0009812    .2385481
      xpers2 |  -.1485804   .0567182    -2.62   0.009     -.259746   -.0374147
       _cons |  -2.000827   .0866883   -23.08   0.000    -2.170733   -1.830921
-------------+----------------------------------------------------------------
    /lnsig2u |  -1.857313   .5903236                     -3.014326   -.7002997
-------------+----------------------------------------------------------------
     sigma_u |   .3950842   .1166138                      .2215376    .7045825
         rho |   .1350166   .0689422                      .0467829    .3317458
------------------------------------------------------------------------------

.                 est store time1 

.                 xtprobit xonset lxyrs lt lnregion xpers2 if year>=1989,vce(clu
> ster gwf_caseid)  

Fitting comparison model:

Iteration 0:   log pseudolikelihood = -346.90883  
Iteration 1:   log pseudolikelihood = -339.26004  
Iteration 2:   log pseudolikelihood = -339.18935  
Iteration 3:   log pseudolikelihood = -339.18934  

Fitting full model:

rho =  0.0     log pseudolikelihood = -339.18934
rho =  0.1     log pseudolikelihood = -337.59483
rho =  0.2     log pseudolikelihood = -338.24464

Iteration 0:   log pseudolikelihood = -337.59483  
Iteration 1:   log pseudolikelihood =  -335.3638  
Iteration 2:   log pseudolikelihood = -331.33194  
Iteration 3:   log pseudolikelihood = -331.13767  
Iteration 4:   log pseudolikelihood = -331.13624  
Iteration 5:   log pseudolikelihood = -331.13624  (backed up)
Iteration 6:   log pseudolikelihood = -331.13622  

Calculating robust standard errors ...

Random-effects probit regression                     Number of obs    =  1,473
Group variable: gwf_caseid                           Number of groups =    138

Random effects u_i ~ Gaussian                        Obs per group:
                                                                  min =      1
                                                                  avg =   10.7
                                                                  max =     22

Integration method: mvaghermite                      Integration pts. =     12

                                                     Wald chi2(4)     =  12.46
Log pseudolikelihood = -331.13622                    Prob > chi2      = 0.0143

                           (Std. err. adjusted for 138 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       lxyrs |   .2841693   .1242465     2.29   0.022     .0406506     .527688
          lt |    .213232   .0934173     2.28   0.022     .0301374    .3963266
    lnregion |   .1383134   .0473716     2.92   0.004     .0454668    .2311601
      xpers2 |  -.2307435    .123084    -1.87   0.061    -.4719836    .0104967
       _cons |  -1.891744    .180119   -10.50   0.000    -2.244771   -1.538717
-------------+----------------------------------------------------------------
    /lnsig2u |   .1898607   .4949993                     -.7803201    1.160041
-------------+----------------------------------------------------------------
     sigma_u |   1.099582   .2721462                      .6769485    1.786075
         rho |   .5473231   .1226413                      .3142509    .7613402
------------------------------------------------------------------------------

.                 est store time2

.                 xtprobit xonset lxyrs time* lt lnregion xpers2,vce(cluster gwf
> _caseid)  

Fitting comparison model:

Iteration 0:   log pseudolikelihood =  -764.5129  
Iteration 1:   log pseudolikelihood =  -735.2932  
Iteration 2:   log pseudolikelihood = -734.47015  
Iteration 3:   log pseudolikelihood = -734.46821  
Iteration 4:   log pseudolikelihood = -734.46821  

Fitting full model:

rho =  0.0     log pseudolikelihood = -734.46821
rho =  0.1     log pseudolikelihood = -732.33765
rho =  0.2     log pseudolikelihood = -737.77412

Iteration 0:   log pseudolikelihood = -732.33758  
Iteration 1:   log pseudolikelihood = -728.61016  
Iteration 2:   log pseudolikelihood = -728.42076  
Iteration 3:   log pseudolikelihood = -728.40518  
Iteration 4:   log pseudolikelihood = -728.40515  

Calculating robust standard errors ...

Random-effects probit regression                     Number of obs    =  4,559
Group variable: gwf_caseid                           Number of groups =    280

Random effects u_i ~ Gaussian                        Obs per group:
                                                                  min =      1
                                                                  avg =   16.3
                                                                  max =     65

Integration method: mvaghermite                      Integration pts. =     12

                                                     Wald chi2(7)     =  51.19
Log pseudolikelihood = -728.40515                    Prob > chi2      = 0.0000

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       lxyrs |   .0228395    .057638     0.40   0.692     -.090129     .135808
        time |  -.0032575   .0278385    -0.12   0.907    -.0578199    .0513049
       time2 |   .0007589    .000901     0.84   0.400    -.0010071    .0025248
       time3 |  -8.76e-06   8.57e-06    -1.02   0.307    -.0000256    8.04e-06
          lt |   .0979598   .0458708     2.14   0.033     .0080547     .187865
    lnregion |   .1278842   .0332869     3.84   0.000     .0626431    .1931253
      xpers2 |  -.1546745   .0488647    -3.17   0.002    -.2504475   -.0589014
       _cons |  -2.285683   .2641588    -8.65   0.000    -2.803424   -1.767941
-------------+----------------------------------------------------------------
    /lnsig2u |   -1.62045   .5087656                     -2.617612   -.6232878
-------------+----------------------------------------------------------------
     sigma_u |   .4447579   .1131388                      .2701423    .7322422
         rho |   .1651428   .0701439                      .0680135    .3490341
------------------------------------------------------------------------------

.                 est store time3

.                 xtprobit xonset lxyrs period* lt lnregion xpers2,vce(cluster g
> wf_caseid)        

Fitting comparison model:

Iteration 0:   log pseudolikelihood =  -764.5129  
Iteration 1:   log pseudolikelihood = -716.19914  
Iteration 2:   log pseudolikelihood = -713.54809  
Iteration 3:   log pseudolikelihood = -713.52857  
Iteration 4:   log pseudolikelihood = -713.52855  

Fitting full model:

rho =  0.0     log pseudolikelihood = -713.52855
rho =  0.1     log pseudolikelihood = -712.44736
rho =  0.2     log pseudolikelihood = -718.03938

Iteration 0:   log pseudolikelihood = -712.44756  
Iteration 1:   log pseudolikelihood = -709.15061  
Iteration 2:   log pseudolikelihood = -709.01457  
Iteration 3:   log pseudolikelihood =   -708.997  
Iteration 4:   log pseudolikelihood = -708.99693  

Calculating robust standard errors ...

Random-effects probit regression                     Number of obs    =  4,559
Group variable: gwf_caseid                           Number of groups =    280

Random effects u_i ~ Gaussian                        Obs per group:
                                                                  min =      1
                                                                  avg =   16.3
                                                                  max =     65

Integration method: mvaghermite                      Integration pts. =     12

                                                     Wald chi2(16)    =  75.98
Log pseudolikelihood = -708.99693                    Prob > chi2      = 0.0000

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       lxyrs |  -.0035939   .0595116    -0.06   0.952    -.1202344    .1130466
     period1 |   .1662682   .4856555     0.34   0.732    -.7855991    1.118135
     period2 |   .7777391    .409613     1.90   0.058    -.0250876    1.580566
     period3 |   .3817379   .4280483     0.89   0.372    -.4572214    1.220697
     period4 |   .8111294    .406995     1.99   0.046     .0134338    1.608825
     period5 |   .2933951   .4292268     0.68   0.494     -.547874    1.134664
     period6 |   .7830691   .4125676     1.90   0.058    -.0255486    1.591687
     period7 |   .6859024   .4130337     1.66   0.097    -.1236287    1.495434
     period8 |   1.309223   .4106065     3.19   0.001     .5044493    2.113997
     period9 |   .7820902   .4198287     1.86   0.062    -.0407589    1.604939
    period10 |   1.034604    .417445     2.48   0.013     .2164269    1.852781
    period11 |   .8848404   .4203965     2.10   0.035     .0608784    1.708802
    period12 |    1.04732   .4188789     2.50   0.012     .2263326    1.868308
          lt |    .098429   .0447322     2.20   0.028     .0107555    .1861024
    lnregion |   .1245443   .0344625     3.61   0.000     .0569991    .1920896
      xpers2 |  -.1561997   .0507394    -3.08   0.002    -.2556471   -.0567523
       _cons |  -2.667388   .3998273    -6.67   0.000    -3.451035   -1.883741
-------------+----------------------------------------------------------------
    /lnsig2u |  -1.745193   .5796435                     -2.881274   -.6091129
-------------+----------------------------------------------------------------
     sigma_u |   .4178651   .1211064                      .2367769    .7374504
         rho |   .1486545   .0733576                      .0530871    .3522616
------------------------------------------------------------------------------

.                 est store time4

.                 xtprobit xonset lxyrs d19* d20  lt lnregion xpers2,vce(cluster
>  gwf_caseid)      

Fitting comparison model:

Iteration 0:   log pseudolikelihood =  -764.5129  
Iteration 1:   log pseudolikelihood = -736.18634  
Iteration 2:   log pseudolikelihood = -735.42211  
Iteration 3:   log pseudolikelihood = -735.42104  
Iteration 4:   log pseudolikelihood = -735.42104  

Fitting full model:

rho =  0.0     log pseudolikelihood = -735.42104
rho =  0.1     log pseudolikelihood = -733.25349
rho =  0.2     log pseudolikelihood =  -738.7774

Iteration 0:   log pseudolikelihood =  -733.2534  
Iteration 1:   log pseudolikelihood = -729.43827  
Iteration 2:   log pseudolikelihood = -729.24995  
Iteration 3:   log pseudolikelihood = -729.23517  
Iteration 4:   log pseudolikelihood = -729.23514  

Calculating robust standard errors ...

Random-effects probit regression                     Number of obs    =  4,559
Group variable: gwf_caseid                           Number of groups =    280

Random effects u_i ~ Gaussian                        Obs per group:
                                                                  min =      1
                                                                  avg =   16.3
                                                                  max =     65

Integration method: mvaghermite                      Integration pts. =     12

                                                     Wald chi2(9)     =  57.64
Log pseudolikelihood = -729.23514                    Prob > chi2      = 0.0000

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       lxyrs |   .0302606   .0600482     0.50   0.614    -.0874316    .1479529
       d1960 |   .2440237   .1723973     1.42   0.157    -.0938688    .5819162
       d1970 |   .1285896   .1750621     0.73   0.463    -.2145257     .471705
       d1980 |    .428799   .1687341     2.54   0.011     .0980863    .7595116
       d1990 |   .5677356   .1979484     2.87   0.004     .1797639    .9557072
       d2000 |   .5779345   .1913676     3.02   0.003     .2028609     .953008
          lt |   .1021744   .0457182     2.23   0.025     .0125684    .1917805
    lnregion |    .126143   .0341905     3.69   0.000      .059131    .1931551
      xpers2 |  -.1524368   .0491999    -3.10   0.002    -.2488667   -.0560068
       _cons |  -2.224335   .1621706   -13.72   0.000    -2.542183   -1.906486
-------------+----------------------------------------------------------------
    /lnsig2u |  -1.628698   .5196886                     -2.647269   -.6101271
-------------+----------------------------------------------------------------
     sigma_u |   .4429276   .1150922                      .2661662    .7370765
         rho |   .1640088   .0712545                      .0661575    .3520302
------------------------------------------------------------------------------

.                 est store time5         

.                 
.                 * Logits *
.                 xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                 logit xonset $cvar xpers2,vce(cluster gwf_caseid)

Iteration 0:   log pseudolikelihood =  -764.5129  
Iteration 1:   log pseudolikelihood = -743.73652  
Iteration 2:   log pseudolikelihood = -740.99576  
Iteration 3:   log pseudolikelihood = -740.99307  
Iteration 4:   log pseudolikelihood = -740.99307  

Logistic regression                                     Number of obs =  4,559
                                                        Wald chi2(5)  =  61.15
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -740.99307                       Pseudo R2     = 0.0308

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     coldwar |  -.4195731   .1867428    -2.25   0.025    -.7855824   -.0535639
       lxyrs |  -.1144817   .0773113    -1.48   0.139     -.266009    .0370455
          lt |   .1719693   .0815333     2.11   0.035      .012167    .3317716
    lnregion |   .2802905   .0696935     4.02   0.000     .1436937    .4168873
      xpers2 |  -.2593459   .0880166    -2.95   0.003    -.4318552   -.0868366
       _cons |  -3.002671   .1534951   -19.56   0.000    -3.303516   -2.701826
------------------------------------------------------------------------------

.                 est store logit1

.                 xtlogit xonset $cvar xpers2,vce(cluster gwf_caseid) 

Fitting comparison model:

Iteration 0:   log pseudolikelihood =  -764.5129  
Iteration 1:   log pseudolikelihood = -743.73652  
Iteration 2:   log pseudolikelihood = -740.99576  
Iteration 3:   log pseudolikelihood = -740.99307  
Iteration 4:   log pseudolikelihood = -740.99307  

Fitting full model:

tau =  0.0     log pseudolikelihood = -740.99307
tau =  0.1     log pseudolikelihood = -738.83298
tau =  0.2     log pseudolikelihood = -737.90505
tau =  0.3     log pseudolikelihood = -738.23902

Iteration 0:   log pseudolikelihood = -737.90505  
Iteration 1:   log pseudolikelihood = -736.77968  
Iteration 2:   log pseudolikelihood = -734.45095  
Iteration 3:   log pseudolikelihood = -734.42284  
Iteration 4:   log pseudolikelihood = -734.42277  

Calculating robust standard errors ...

Random-effects logistic regression                   Number of obs    =  4,559
Group variable: gwf_caseid                           Number of groups =    280

Random effects u_i ~ Gaussian                        Obs per group:
                                                                  min =      1
                                                                  avg =   16.3
                                                                  max =     65

Integration method: mvaghermite                      Integration pts. =     12

                                                     Wald chi2(5)     =  51.57
Log pseudolikelihood = -734.42277                    Prob > chi2      = 0.0000

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     coldwar |  -.6556602    .242948    -2.70   0.007     -1.13183   -.1794909
       lxyrs |   .1206091   .1263571     0.95   0.340    -.1270464    .3682645
          lt |   .2125342   .1005283     2.11   0.035     .0155024     .409566
    lnregion |   .2895158   .0726953     3.98   0.000     .1470357    .4319959
      xpers2 |  -.3332718   .1030914    -3.23   0.001    -.5353272   -.1312164
       _cons |   -3.05845   .1845884   -16.57   0.000    -3.420237   -2.696664
-------------+----------------------------------------------------------------
    /lnsig2u |  -.1455264   .5083446                     -1.141863    .8508108
-------------+----------------------------------------------------------------
     sigma_u |    .929821   .2363347                      .5649988    1.530211
         rho |    .208107   .0837744                      .0884498    .4158006
------------------------------------------------------------------------------

.                 est store logit2

.                 replace lpop = lpop*5
(4,510 real changes made)

.                 xtlogit xonset $cvar xpers2 m_coldwar m_lxyrs m_lt m_xpers2  m
> _lnregion,vce(cluster gwf_caseid)

Fitting comparison model:

Iteration 0:   log pseudolikelihood =  -764.5129  
Iteration 1:   log pseudolikelihood =  -680.2077  
Iteration 2:   log pseudolikelihood =  -645.6474  
Iteration 3:   log pseudolikelihood = -645.09953  
Iteration 4:   log pseudolikelihood =   -645.099  
Iteration 5:   log pseudolikelihood =   -645.099  

Fitting full model:

tau =  0.0     log pseudolikelihood =   -645.099
tau =  0.1     log pseudolikelihood = -646.76845

Iteration 0:   log pseudolikelihood = -646.76845  
Iteration 1:   log pseudolikelihood = -645.46777  
Iteration 2:   log pseudolikelihood = -645.19791  
Iteration 3:   log pseudolikelihood = -645.11932  
Iteration 4:   log pseudolikelihood = -645.10361  
Iteration 5:   log pseudolikelihood = -645.10007  
Iteration 6:   log pseudolikelihood = -645.09924  
Iteration 7:   log pseudolikelihood = -645.09905  
Iteration 8:   log pseudolikelihood = -645.09905  
Iteration 9:   log pseudolikelihood = -645.09904  

Calculating robust standard errors ...

Random-effects logistic regression                   Number of obs    =  4,559
Group variable: gwf_caseid                           Number of groups =    280

Random effects u_i ~ Gaussian                        Obs per group:
                                                                  min =      1
                                                                  avg =   16.3
                                                                  max =     65

Integration method: mvaghermite                      Integration pts. =     12

                                                     Wald chi2(10)    = 184.10
Log pseudolikelihood = -645.09904                    Prob > chi2      = 0.0000

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     coldwar |  -1.319162   .4650879    -2.84   0.005    -2.230717   -.4076063
       lxyrs |   1.023912   .2402288     4.26   0.000     .5530722    1.494752
          lt |   .4112842   .1943311     2.12   0.034     .0304023    .7921661
    lnregion |   .3017445   .0782021     3.86   0.000     .1484712    .4550178
      xpers2 |  -.3574189   .2056778    -1.74   0.082    -.7605399    .0457022
   m_coldwar |   1.029123    .561979     1.83   0.067    -.0723355    2.130582
     m_lxyrs |  -2.242466   .3584824    -6.26   0.000    -2.945079   -1.539854
        m_lt |  -.6107627   .3567306    -1.71   0.087    -1.309942    .0884163
    m_xpers2 |   .4485055    .234967     1.91   0.056    -.0120213    .9090323
  m_lnregion |  -.3213826    .271128    -1.19   0.236    -.8527838    .2100186
       _cons |  -3.727682   .2744439   -13.58   0.000    -4.265583   -3.189782
-------------+----------------------------------------------------------------
    /lnsig2u |  -12.83481   122956.8                     -241003.8    240978.1
-------------+----------------------------------------------------------------
     sigma_u |   .0016329   100.3874                             0           .
         rho |   8.10e-07    .099652                             0           .
------------------------------------------------------------------------------

.                 est store logit3

.                 margins,dydx(xpers2)

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(xonset=1), predict(pr)
dy/dx wrt:  xpers2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0126757   .0074554    -1.70   0.089     -.027288    .0019366
------------------------------------------------------------------------------

.                 firthlogit xonset $cvar xpers2 m_coldwar mp_lxyrs mp_lt mp_xpe
> rs2 mp_lnregion mp_xonset

initial:       penalized log likelihood = -743.68511
rescale:       penalized log likelihood = -743.68511
Iteration 0:   penalized log likelihood = -743.68511  
Iteration 1:   penalized log likelihood = -630.99696  
Iteration 2:   penalized log likelihood = -587.53087  
Iteration 3:   penalized log likelihood = -584.55195  
Iteration 4:   penalized log likelihood = -584.52571  
Iteration 5:   penalized log likelihood = -584.52571  

                                                        Number of obs =  4,559
                                                        Wald chi2(11) = 256.18
Penalized log likelihood = -584.52571                   Prob > chi2   = 0.0000

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     coldwar |   -1.27864   .2673713    -4.78   0.000    -1.802678   -.7546022
       lxyrs |   .6821388   .1219699     5.59   0.000     .4430821    .9211954
          lt |    .263575   .1181857     2.23   0.026     .0319352    .4952148
    lnregion |   .2955615   .0776361     3.81   0.000     .1433975    .4477255
      xpers2 |  -.2357798   .1333142    -1.77   0.077    -.4970708    .0255112
   m_coldwar |   1.904817   .4411675     4.32   0.000     1.040145     2.76949
    mp_lxyrs |  -1.245947   .2390675    -5.21   0.000    -1.714511   -.7773832
       mp_lt |   .2521015   .2276626     1.11   0.268     -.194109    .6983119
   mp_xpers2 |   .0852504   .1867964     0.46   0.648    -.2808638    .4513646
 mp_lnregion |  -.0814636   .3108133    -0.26   0.793    -.6906464    .5277192
   mp_xonset |   12.13118   1.397505     8.68   0.000     9.392118    14.87023
       _cons |  -4.828914   .2877329   -16.78   0.000     -5.39286   -4.264968
------------------------------------------------------------------------------

.                 est store logit4

.                 margins,dydx(xpers2)

Average marginal effects                                 Number of obs = 4,559
Model VCE: OIM

Expression: Linear prediction, predict()
dy/dx wrt:  xpers2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.2357798   .1333142    -1.77   0.077    -.4970708    .0255112
------------------------------------------------------------------------------

.                 
.                 * Cook et al approach to unit intercepts placed in a probit *
.                  probit xonset i.fe $cvar xpers2,vce(cluster gwf_caseid)

Iteration 0:   log pseudolikelihood =  -764.5129  
Iteration 1:   log pseudolikelihood =  -605.8446  
Iteration 2:   log pseudolikelihood = -573.02087  
Iteration 3:   log pseudolikelihood = -570.60464  
Iteration 4:   log pseudolikelihood = -570.58624  
Iteration 5:   log pseudolikelihood = -570.58623  

Probit regression                                       Number of obs =  4,559
                                                        Wald chi2(5)  =      .
                                                        Prob > chi2   =      .
Log pseudolikelihood = -570.58623                       Pseudo R2     = 0.2537

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
          fe |
          6  |   1.549514   .2735104     5.67   0.000     1.013444    2.085585
          7  |   1.375738    .264148     5.21   0.000     .8580171    1.893458
         14  |   2.549482   .3467787     7.35   0.000     1.869809    3.229156
         15  |   2.719505   .4257416     6.39   0.000     1.885067    3.553943
         16  |   2.567876   .4062612     6.32   0.000     1.771619    3.364133
         17  |   1.960621   .3179673     6.17   0.000     1.337416    2.583825
         19  |   2.206985   .3321473     6.64   0.000     1.555988    2.857981
         23  |   2.936348   .3355981     8.75   0.000     2.278588    3.594108
         25  |   2.567612    .330226     7.78   0.000     1.920381    3.214843
         26  |   3.822489   .4642801     8.23   0.000     2.912516    4.732461
         31  |    1.91988   .3082319     6.23   0.000     1.315757    2.524004
         35  |   2.214957   .3308082     6.70   0.000     1.566584    2.863329
         38  |   3.104401   .3676827     8.44   0.000     2.383756    3.825046
         39  |   4.353644   .5332271     8.16   0.000     3.308538     5.39875
         41  |   2.714984   .4272752     6.35   0.000      1.87754    3.552428
         42  |   1.565531   .2857721     5.48   0.000     1.005427    2.125634
         43  |   2.923562   .4147445     7.05   0.000     2.110677    3.736446
         55  |   .8760046   .1991391     4.40   0.000     .4856992     1.26631
         57  |   1.335716   .2236333     5.97   0.000     .8974026    1.774029
         67  |   2.354958   .3852606     6.11   0.000     1.599862    3.110055
         68  |   1.867047   .2980119     6.27   0.000     1.282954    2.451139
         70  |    2.96312   .3914569     7.57   0.000     2.195879    3.730362
         72  |   3.694956   .4716549     7.83   0.000      2.77053    4.619383
         74  |    1.66638     .28202     5.91   0.000     1.113631    2.219129
         75  |   1.223899   .2636333     4.64   0.000     .7071874    1.740611
         80  |   1.876392   .3318757     5.65   0.000     1.225928    2.526857
         81  |   2.156376   .3630544     5.94   0.000     1.444803     2.86795
         85  |   3.007669   .3361518     8.95   0.000     2.348824    3.666515
         89  |   1.502877   .2533837     5.93   0.000     1.006254      1.9995
         91  |   1.974675   .3611006     5.47   0.000     1.266931     2.68242
         96  |   .7513685   .1747801     4.30   0.000     .4088058    1.093931
         97  |   1.120659   .2454992     4.56   0.000     .6394898    1.601829
        101  |   1.291815   .2650863     4.87   0.000     .7722553    1.811375
        102  |   1.772386   .3133155     5.66   0.000     1.158299    2.386474
        106  |   .9808139   .1754837     5.59   0.000     .6368721    1.324756
        107  |   2.925184   .4131795     7.08   0.000     2.115367    3.735001
        108  |   3.434433   .4630969     7.42   0.000      2.52678    4.342086
        113  |   1.347642   .2660083     5.07   0.000     .8262758    1.869009
        114  |   3.481895   .4276004     8.14   0.000     2.643814    4.319977
        116  |   .8113875   .2006733     4.04   0.000     .4180751      1.2047
        121  |    2.97378   .4127147     7.21   0.000     2.164874    3.782686
        122  |   2.206133   .3394786     6.50   0.000     1.540767    2.871499
        124  |   3.855574   .4064664     9.49   0.000     3.058915    4.652234
        126  |   2.536679    .315863     8.03   0.000     1.917599    3.155759
        130  |   1.598906   .3331887     4.80   0.000     .9458681    2.251944
        132  |   2.255301   .3049522     7.40   0.000     1.657606    2.852997
        133  |   1.597872   .3177206     5.03   0.000     .9751513    2.220593
        134  |   1.437537   .2417171     5.95   0.000     .9637803    1.911294
        141  |   .9995437   .2676379     3.73   0.000     .4749831    1.524104
        143  |    .820693   .2393752     3.43   0.001     .3515262     1.28986
        145  |   1.503156   .2750631     5.46   0.000     .9640422     2.04227
        147  |   2.265479   .3492981     6.49   0.000     1.580867    2.950091
        148  |   2.444457   .3574248     6.84   0.000     1.743917    3.144997
        149  |   1.359301   .2502077     5.43   0.000     .8689028    1.849699
        150  |   2.294154   .3368002     6.81   0.000     1.634038    2.954271
        151  |   1.751555    .295078     5.94   0.000     1.173213    2.329897
        163  |   2.229801   .3735734     5.97   0.000     1.497611    2.961992
        165  |   1.413532    .255282     5.54   0.000     .9131886    1.913876
        166  |   1.164509   .2250973     5.17   0.000     .7233264    1.605691
        167  |   1.130105   .2255043     5.01   0.000     .6881245    1.572085
        169  |   1.328204   .2321833     5.72   0.000     .8731331    1.783275
        174  |   1.376941   .3171805     4.34   0.000     .7552787    1.998604
        175  |   1.006004   .3073737     3.27   0.001     .4035629    1.608446
        176  |   .6689616   .1994589     3.35   0.001     .2780294    1.059894
        179  |   1.626329   .3037155     5.35   0.000     1.031057      2.2216
        180  |   1.099404   .2470811     4.45   0.000      .615134    1.583674
        184  |   1.208178   .2536011     4.76   0.000     .7111294    1.705227
        185  |   2.039823   .3359164     6.07   0.000     1.381439    2.698208
        188  |   1.666279   .2643789     6.30   0.000     1.148106    2.184452
        191  |   2.369882   .2879621     8.23   0.000     1.805487    2.934277
        195  |   2.616044   .3552413     7.36   0.000     1.919784    3.312304
        196  |   3.829595   .3775493    10.14   0.000     3.089612    4.569578
        197  |   2.997425   .4370871     6.86   0.000     2.140751      3.8541
        198  |     1.3543   .2321457     5.83   0.000     .8993033    1.809298
        199  |   4.505824    .496734     9.07   0.000     3.532243    5.479404
        202  |   3.227518   .3842327     8.40   0.000     2.474436    3.980601
        205  |   1.341397   .2831468     4.74   0.000     .7864395    1.896355
        208  |   2.044713   .3159345     6.47   0.000     1.425493    2.663933
        209  |   1.450126   .2205786     6.57   0.000     1.017799    1.882452
        210  |   2.355244   .3724751     6.32   0.000     1.625206    3.085282
        211  |   2.669913   .3988993     6.69   0.000     1.888085    3.451742
        212  |   1.562565   .3058049     5.11   0.000     .9631983    2.161932
        213  |   1.831881   .3113244     5.88   0.000     1.221697    2.442066
        214  |   1.511844   .2857532     5.29   0.000     .9517778     2.07191
        219  |   1.623254   .3030193     5.36   0.000     1.029347    2.217161
        220  |   2.522755   .3482689     7.24   0.000      1.84016    3.205349
        222  |   1.813072   .2942915     6.16   0.000     1.236272    2.389873
        227  |   1.630925   .3065057     5.32   0.000     1.030184    2.231665
        229  |   2.973306   .4082743     7.28   0.000     2.173103    3.773509
        231  |   1.982667   .3339221     5.94   0.000     1.328192    2.637143
        232  |   2.286368   .3535893     6.47   0.000     1.593345     2.97939
        234  |   2.872585   .4298777     6.68   0.000     2.030041     3.71513
        235  |   2.284189   .3558282     6.42   0.000     1.586779      2.9816
        238  |   .6315738   .1695405     3.73   0.000     .2992805     .963867
        245  |   1.712932   .3014785     5.68   0.000     1.122045    2.303818
        247  |   1.154195   .2685444     4.30   0.000      .627858    1.680533
        251  |   1.838389   .3024578     6.08   0.000     1.245582    2.431195
        255  |   1.224148   .2381328     5.14   0.000     .7574159    1.690879
        256  |   .8906934   .2248812     3.96   0.000     .4499344    1.331452
        258  |   3.192854   .3450121     9.25   0.000     2.516642    3.869065
        267  |   2.542508   .3953395     6.43   0.000     1.767657    3.317359
        269  |      1.951   .2576933     7.57   0.000      1.44593     2.45607
        270  |    2.72979   .3989986     6.84   0.000     1.947767    3.511813
        277  |   2.327247   .3593549     6.48   0.000     1.622925     3.03157
        278  |   1.436994   .2896367     4.96   0.000     .8693162    2.004671
        279  |   1.335113   .2888861     4.62   0.000     .7689064    1.901319
        280  |   1.811181   .2820195     6.42   0.000     1.258433    2.363929
             |
     coldwar |  -.7864687   .2108005    -3.73   0.000     -1.19963   -.3733074
       lxyrs |   .3060005   .0884386     3.46   0.001     .1326641    .4793369
          lt |   .1251921   .0748587     1.67   0.094    -.0215282    .2719124
    lnregion |   .1419353   .0419146     3.39   0.001     .0597843    .2240864
      xpers2 |  -.1327317   .0800973    -1.66   0.097    -.2897195    .0242561
       _cons |  -2.760474   .2453885   -11.25   0.000    -3.241427   -2.279522
------------------------------------------------------------------------------

.                  
.                 * Drop each region *
.                 use temp-fe,clear

.                 xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                 local region = "cacar casia ceeurope easia meast nafrica samer
> ica ssafrica weu"

.                 local i =1

.                 foreach v of local region {
  2.                         qui xtprobit xonset $cvar xpers2 if region~="`v'",r
> e vce(cluster gwf_caseid)
  3.                         lincom xpers2
  4.                         est store region`i'
  5.                         local i =`i' +1
  6.                  }

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.2042356   .0527954    -3.87   0.000    -.3077126   -.1007585
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1599445    .050901    -3.14   0.002    -.2597087   -.0601804
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1210288   .0557673    -2.17   0.030    -.2303307   -.0117268
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1512541   .0509999    -2.97   0.003    -.2512121   -.0512961
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1404557    .047984    -2.93   0.003    -.2345026   -.0464089
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1557292   .0497654    -3.13   0.002    -.2532676   -.0581908
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1486687   .0511657    -2.91   0.004    -.2489515   -.0483858
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1725833   .0579384    -2.98   0.003    -.2861406   -.0590261
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1469879   .0484355    -3.03   0.002    -.2419197   -.0520561
------------------------------------------------------------------------------

.                  
.                 * Drop each decade *
.                   qui xtprobit xonset $cvar xpers2 if year>=1960,re vce(cluste
> r gwf_caseid)

.                   lincom xpers2

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1515804    .050261    -3.02   0.003    -.2500903   -.0530706
------------------------------------------------------------------------------

.                   est store decade1

.                   local decade = "1960 1970 1980 1990 2000"

.                   local i =2

.                   foreach v of local decade {
  2.                         qui xtprobit xonset $cvar xpers2 if d`v'==0,re vce(
> cluster gwf_caseid)
  3.                         lincom xpers2
  4.                         est store decade`i'
  5.                         local i =`i' +1
  6.                   }

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1268446   .0510199    -2.49   0.013    -.2268417   -.0268475
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1693586   .0539494    -3.14   0.002    -.2750976   -.0636197
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1652168   .0571672    -2.89   0.004    -.2772624   -.0531712
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1376759   .0492919    -2.79   0.005    -.2342862   -.0410657
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1630067   .0513315    -3.18   0.001    -.2636145   -.0623989
------------------------------------------------------------------------------

.           
.                   * Duration time *
.                   xtprobit xonset $cvar xpers2,vce(cluster gwf_caseid)

Fitting comparison model:

Iteration 0:   log pseudolikelihood =  -764.5129  
Iteration 1:   log pseudolikelihood = -740.91043  
Iteration 2:   log pseudolikelihood = -740.40707  
Iteration 3:   log pseudolikelihood = -740.40684  
Iteration 4:   log pseudolikelihood = -740.40684  

Fitting full model:

rho =  0.0     log pseudolikelihood = -740.40684
rho =  0.1     log pseudolikelihood = -737.93392
rho =  0.2     log pseudolikelihood = -743.36799

Iteration 0:   log pseudolikelihood = -737.93385  
Iteration 1:   log pseudolikelihood = -734.13573  
Iteration 2:   log pseudolikelihood = -733.96097  
Iteration 3:   log pseudolikelihood = -733.95029  
Iteration 4:   log pseudolikelihood = -733.95028  

Calculating robust standard errors ...

Random-effects probit regression                     Number of obs    =  4,559
Group variable: gwf_caseid                           Number of groups =    280

Random effects u_i ~ Gaussian                        Obs per group:
                                                                  min =      1
                                                                  avg =   16.3
                                                                  max =     65

Integration method: mvaghermite                      Integration pts. =     12

                                                     Wald chi2(5)     =  48.70
Log pseudolikelihood = -733.95028                    Prob > chi2      = 0.0000

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     coldwar |  -.3034941   .1097552    -2.77   0.006    -.5186104   -.0883778
       lxyrs |   .0519927   .0571514     0.91   0.363     -.060022    .1640075
          lt |   .1062064   .0460377     2.31   0.021     .0159741    .1964387
    lnregion |   .1391665   .0346306     4.02   0.000     .0712917    .2070413
      xpers2 |  -.1550143   .0482532    -3.21   0.001    -.2495889   -.0604397
       _cons |  -1.674404   .0843464   -19.85   0.000     -1.83972   -1.509088
-------------+----------------------------------------------------------------
    /lnsig2u |  -1.642953   .5106928                     -2.643893   -.6420137
-------------+----------------------------------------------------------------
     sigma_u |   .4397818   .1122967                      .2666159    .7254183
         rho |   .1620636   .0693516                      .0663664    .3447915
------------------------------------------------------------------------------

.                   xtprobit xonset coldwar xyrs* lt lnregion xpers2,vce(cluster
>  gwf_caseid)

Fitting comparison model:

Iteration 0:   log pseudolikelihood =  -764.5129  
Iteration 1:   log pseudolikelihood = -739.03366  
Iteration 2:   log pseudolikelihood = -738.42587  
Iteration 3:   log pseudolikelihood = -738.42533  
Iteration 4:   log pseudolikelihood = -738.42533  

Fitting full model:

rho =  0.0     log pseudolikelihood = -738.42533
rho =  0.1     log pseudolikelihood = -736.30443
rho =  0.2     log pseudolikelihood = -741.87531

Iteration 0:   log pseudolikelihood = -736.30433  
Iteration 1:   log pseudolikelihood = -732.64522  
Iteration 2:   log pseudolikelihood = -732.47496  
Iteration 3:   log pseudolikelihood = -732.45918  
Iteration 4:   log pseudolikelihood = -732.45914  

Calculating robust standard errors ...

Random-effects probit regression                     Number of obs    =  4,559
Group variable: gwf_caseid                           Number of groups =    280

Random effects u_i ~ Gaussian                        Obs per group:
                                                                  min =      1
                                                                  avg =   16.3
                                                                  max =     65

Integration method: mvaghermite                      Integration pts. =     12

                                                     Wald chi2(7)     =  50.14
Log pseudolikelihood = -732.45914                    Prob > chi2      = 0.0000

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     coldwar |  -.3193021   .1116312    -2.86   0.004    -.5380953   -.1005089
        xyrs |  -.0060638   .0191706    -0.32   0.752    -.0436374    .0315098
       xyrs2 |   .0008355   .0008532     0.98   0.327    -.0008367    .0025077
       xyrs3 |  -.0000145   .0000108    -1.34   0.179    -.0000355    6.62e-06
          lt |   .1076194   .0457804     2.35   0.019     .0178914    .1973474
    lnregion |   .1331894   .0339964     3.92   0.000     .0665576    .1998211
      xpers2 |   -.154067   .0482801    -3.19   0.001    -.2486942   -.0594397
       _cons |  -1.708702   .1311892   -13.02   0.000    -1.965828   -1.451576
-------------+----------------------------------------------------------------
    /lnsig2u |  -1.682748   .4915584                     -2.646184   -.7193111
-------------+----------------------------------------------------------------
     sigma_u |   .4311178   .1059598                      .2663105    .6979167
         rho |    .156732   .0649678                      .0662246    .3275447
------------------------------------------------------------------------------

.                   gen lnXxpers =lxyrs*xpers2

.                   qui xtprobit xonset coldwar lxyrs lt lnregion xpers2 lnXxper
> s,vce(cluster gwf_caseid)

.                   test lnXxpers

 ( 1)  [xonset]lnXxpers = 0

           chi2(  1) =    0.01
         Prob > chi2 =    0.9159

.                   gen y1Xxpers = xpers2*xyrs

.                   gen y2Xxpers = xpers2*xyrs2

.                   gen y3Xxpers = xpers2*xyrs3

.                   qui xtprobit xonset coldwar xyrs* y1X y2X y3X lt lnregion xp
> ers2,vce(cluster gwf_caseid)

.                   test y1 y2 y3

 ( 1)  [xonset]y1Xxpers = 0
 ( 2)  [xonset]y2Xxpers = 0
 ( 3)  [xonset]y3Xxpers = 0

           chi2(  3) =    2.34
         Prob > chi2 =    0.5051

.                   
.                   * Alternative coding of DV *
.                   use temp-fe,clear

.                   gen xonsetNAV13 =  nav13_start==1 

.                   gen xonsetNAV21 =  nav21_start==1 

.                   gen xonsetMEC = nvcstart 
(348 missing values generated)

.                   * This comes from the Ulfelder and Chenoweth 2015 data:  
.                   * "Major Episodes of Contention (MEC) data set to identify t
> he onset of thesenonviolent episodes"
.                   gen xonsetREN = xonset*renavco 
(4,226 missing values generated)

.                   recode xonsetREN (.=0)
(4226 changes made to xonsetREN)

.                   tab xonset xonsetNAV13

 Non-viol. |
   protest |
  campaign |      xonsetNAV13
     onset |         0          1 |     Total
-----------+----------------------+----------
         0 |     4,349         28 |     4,377 
         1 |        91         91 |       182 
-----------+----------------------+----------
     Total |     4,440        119 |     4,559 

.                   tab xonset xonsetNAV21

 Non-viol. |
   protest |
  campaign |      xonsetNAV21
     onset |         0          1 |     Total
-----------+----------------------+----------
         0 |     4,352         25 |     4,377 
         1 |       111         71 |       182 
-----------+----------------------+----------
     Total |     4,463         96 |     4,559 

.                   tab xonset xonsetMEC

 Non-viol. |
   protest |
  campaign |       xonsetMEC
     onset |         0          1 |     Total
-----------+----------------------+----------
         0 |     4,015         17 |     4,032 
         1 |        89         90 |       179 
-----------+----------------------+----------
     Total |     4,104        107 |     4,211 

.                   tab xonsetMEC xonsetNAV13

           |      xonsetNAV13
 xonsetMEC |         0          1 |     Total
-----------+----------------------+----------
         0 |     4,081         23 |     4,104 
         1 |        13         94 |       107 
-----------+----------------------+----------
     Total |     4,094        117 |     4,211 

.                   tab xonsetMEC xonsetNAV21

           |      xonsetNAV21
 xonsetMEC |         0          1 |     Total
-----------+----------------------+----------
         0 |     4,083         21 |     4,104 
         1 |        33         74 |       107 
-----------+----------------------+----------
     Total |     4,116         95 |     4,211 

.                   
.                   xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                   btscs xonsetMEC year cow,gen(yrsMEC)

.                   btscs xonsetNAV13 year cow,gen(yrsNAV13)

.                   btscs xonsetNAV21 year cow,gen(yrsNAV21)

.                   btscs xonsetREN year cow,gen(yrsREN)

. 
.                   sum yrs*

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      yrsMEC |      4,211    16.88625    13.37385          0         55
    yrsNAV13 |      4,559    17.57403    14.25148          0         64
    yrsNAV21 |      4,559    18.71024     14.6086          0         64
      yrsREN |      4,559    15.82365    13.63223          0         64

.                   gen lyrsNAV13 =ln(yrsNAV13+1)

.                   gen lyrsNAV21 =ln(yrsNAV21+1)

.                   gen lyrsMEC =ln(yrsMEC+1)
(348 missing values generated)

.                   gen lyrsREN =ln(yrsREN+1)

. 
.                   gen yrsMEC2 = yrsMEC^2
(348 missing values generated)

.                   gen yrsMEC3 = yrsMEC^3
(348 missing values generated)

.                   gen yrsNAV212 = yrsNAV21^2

.                   gen yrsNAV213 = yrsNAV21^3

.                   gen yrsNAV132 = yrsNAV13^2

.                   gen yrsNAV133 = yrsNAV13^3

.                   gen yrsREN2 = yrsREN^2

.                   gen yrsREN3 = yrsREN^3

. 
.                   qui:xtprobit xonsetMEC lyrsMEC period* lnregion lt lpop xper
> s2,vce(cluster gwf_caseid)

.                   lincom xpers2

 ( 1)  [xonsetMEC]xpers2 = 0

------------------------------------------------------------------------------
   xonsetMEC | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1455975   .0541778    -2.69   0.007     -.251784   -.0394109
------------------------------------------------------------------------------

.                   est store alt1

.                   qui:xtprobit xonsetMEC yrsMEC* period* lnregion lt xpers2,vc
> e(cluster gwf_caseid)

.                   lincom xpers2

 ( 1)  [xonsetMEC]xpers2 = 0

------------------------------------------------------------------------------
   xonsetMEC | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1774207    .061862    -2.87   0.004     -.298668   -.0561734
------------------------------------------------------------------------------

.                   qui:xtprobit xonsetNAV13 lyrsNAV13 period* lnregion lt xpers
> 2,vce(cluster gwf_caseid)

.                   lincom xpers2

 ( 1)  [xonsetNAV13]xpers2 = 0

------------------------------------------------------------------------------
 xonsetNAV13 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1258043   .0532842    -2.36   0.018    -.2302395   -.0213692
------------------------------------------------------------------------------

.                   est store alt2

.                   qui:xtprobit xonsetNAV13 yrsNAV13* period* lnregion lt xpers
> 2,vce(cluster gwf_caseid)

.                   lincom xpers2

 ( 1)  [xonsetNAV13]xpers2 = 0

------------------------------------------------------------------------------
 xonsetNAV13 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1218294   .0528989    -2.30   0.021    -.2255093   -.0181495
------------------------------------------------------------------------------

.                   qui:xtprobit xonsetNAV21 lyrsNAV21 period* lnregion lt xpers
> 2,vce(cluster gwf_caseid)

.                   lincom xpers2

 ( 1)  [xonsetNAV21]xpers2 = 0

------------------------------------------------------------------------------
 xonsetNAV21 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1351158   .0567864    -2.38   0.017    -.2464151   -.0238165
------------------------------------------------------------------------------

.                   est store alt3

.                   qui:xtprobit xonsetNAV21 yrsNAV21* period* lnregion lt xpers
> 2,vce(cluster gwf_caseid)

.                   lincom xpers2

 ( 1)  [xonsetNAV21]xpers2 = 0

------------------------------------------------------------------------------
 xonsetNAV21 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1269354   .0561843    -2.26   0.024    -.2370546   -.0168161
------------------------------------------------------------------------------

.                   qui:xtprobit xonsetREN lyrsREN period* lnregion lt xpers2,vc
> e(cluster gwf_caseid)

.                   lincom xpers2

 ( 1)  [xonsetREN]xpers2 = 0

------------------------------------------------------------------------------
   xonsetREN | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1477759   .0490616    -3.01   0.003     -.243935   -.0516169
------------------------------------------------------------------------------

.                   est store alt4

.                   qui:xtprobit xonsetREN yrsREN* period* lnregion lt xpers2,vc
> e(cluster gwf_caseid)

.                   lincom xpers2

 ( 1)  [xonsetREN]xpers2 = 0

------------------------------------------------------------------------------
   xonsetREN | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1420573   .0475272    -2.99   0.003     -.235209   -.0489056
------------------------------------------------------------------------------

.                   qui:xtprobit xonset  lxyrs period* lnregion lt xpers2,vce(cl
> uster gwf_caseid)

.                   est store alt0

.                   
.                   
.                   **********************************
.                   ************ Plots ***************
.                   **********************************  
.                         * Reported RE & CRE models coefficient plot *
.                         coefplot (b0, msym(S)) (b1, msym(d)) (b2, msym(t)) (b3
> , msym(O)) (b4, msym(oh)), ///
>                                 title("Personalization and Non-violent protest
> ", size(small)) ///
>                                 drop(_cons xyr* lxyr* m_* mXy_* y_* ) ///
>                                 order(gwf_personal xpers xpers1 xpers2) xline(
> 0) grid(glcolor(gs13)) ///
>                                 mfcolor(white) xlabel(-.8(.2).4,labsize(vsmall
> ))levels(95 90)  ///
>                                 legend(lab(3 "Probit")lab(6 "RE probit")lab(9 
> "CRE" "probit") ///
>                                 lab(12 "Two-way CRE" "probit")lab(15 "Interact
> ive CRE"  "probit")  ///
>                                 size(vsmall)pos(7)ring(0)) ysize(3) xsize(3.5)
>  saving(r1, replace) ///
>                                 xtitle("Coefficient estimate", size(small) hei
> ght(6)) ///
>                                 note("90 (thick) and 95 (thin) percent confide
> nce intervals", size(vsmall) pos(6))
(file r1.gph not found)
file r1.gph saved

.                                 graph export "$dir\base-models.pdf", as(pdf) r
> eplace
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\base-models.pdf saved as PDF format

.                         
.                         * Personalism variables models coefficient plot *
.                         coefplot (p1, msym(d)) (p2, msym(t)) (p3, msym(oh)) (p
> 4, msym(plus)) (p5, msym(P)) (p6, msym(T)), ///
>                                 title("Personalization and Non-violent protest
> ", size(small)) drop(_cons xyr* lxyr* ) ///
>                                 order(gwf_personal xpers xpers1 xpers2) xline(
> 0) grid(glcolor(gs13)) mfcolor(white) xlabel(-.4(.1).3,labsize(vsmall))  level
> s(95 90)  ///
>                                 legend(lab(3 "Regime type" "logit") lab(6 "Per
> sonalization" "logit") lab(9 "Personalization"  "RE logit") lab(12 "Party pers
> ." "RE logit") ///
>                                 lab(15  "Security pers." "RE logit") lab(18 "B
> oth types" "personalism") size(vsmall)) ysize(3) xsize(3.5) saving(r1, replace
> )   xtitle("Coefficient estimate", size(small) height(6)) ///
>                                 note("90 (thick) and 95 (thin) percent confide
> nce intervals", size(vsmall) pos(6))
(note:  named style P not found in class symbol, default attributes used)
file r1.gph saved

.                                 graph export "$dir\personalism-models.pdf", as
> (pdf)   replace
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\personalism-models.pdf saved as PDF format

.                         
.                                 
.                         * Logit models coefficient plot *
.                         coefplot (logit1, msym(d)) (logit2, msym(t)) (logit4, 
> msym(plus)), ///
>                                 title("Personalization and Non-violent protest
> ", size(small)) drop(_cons lxyr* m* coldwar ) ///
>                                 order(xpers2 lt lpopl lnregion) xline(0) grid(
> glcolor(gs13)) mfcolor(white) xlabel(-.75(.25).75,labsize(vsmall))  levels(95 
> 90)  ///
>                                 legend(lab(3 "Logit") lab(6 "RE" "logit")   la
> b(9 "Firth" "logit") size(vsmall))ysize(3) xsize(3.5) ///
>                                 saving(r1, replace)     xtitle("Coefficient es
> timate", size(small) height(6)) note("90 (thick) and 95 (thin) percent confide
> nce intervals", size(vsmall) pos(6))
file r1.gph saved

.                                 graph export "$dir\logit-models.pdf", as(pdf) 
>   replace
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\logit-models.pdf saved as PDF format

. 
.                                 
.                                 * LPMs(WFE from Imai and Kim 2019 uses R scrip
> t pasted below) *
.                                          use temp-fe,clear

.                                          keep if sample==1
(0 observations deleted)

.                                          * set the binary treatment variable a
> t the median of security personalism *
.                                          qui sum xpers2 if sample==1,detail

.                                          local m=r(p50)

.                                          gen treat1=xpers2>=(`m'-.0001) 

.                                             * Note: using the IRT-2PL instead 
> of mixed yields same treatment variable 
.                                                 qui sum irtpers2 if sample==1,
> detail

.                                                 local m=r(p50)

.                                                 di `m'
.45907182

.                                                 gen treat2=irtpers2>=(`m'-.000
> 1) 

.                                                 tab treat2 treat1

           |        treat1
    treat2 |         0          1 |     Total
-----------+----------------------+----------
         0 |     2,275          0 |     2,275 
         1 |         0      2,284 |     2,284 
-----------+----------------------+----------
     Total |     2,275      2,284 |     4,559 

.                                                 drop treat2 

.                                          xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                                          gen l1treat1=l.treat1
(280 missing values generated)

.                                          gen l2treat1=l2.treat1
(536 missing values generated)

.                                          gen l3treat1=l3.treat1
(770 missing values generated)

. 
.                                          gen l1xonset=l.xonset
(280 missing values generated)

.                                          gen l2xonset=l2.xonset
(536 missing values generated)

.                                          gen l3xonset=l3.xonset
(770 missing values generated)

.                                          
.                                          * Bivariate *
.                                          ttest xonset,by(treat1)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       0 |   2,275    .0457143      .00438    .2089107    .0371252    .0543034
       1 |   2,284    .0341506     .003801    .1816557    .0266968    .0416044
---------+--------------------------------------------------------------------
Combined |   4,559     .039921    .0028998    .1957952     .034236     .045606
---------+--------------------------------------------------------------------
    diff |            .0115637    .0057977                .0001974      .02293
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   1.9945
H0: diff = 0                                     Degrees of freedom =     4557

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9769         Pr(|T| > |t|) = 0.0462          Pr(T > t) = 0.0231

.                                          ttest xongoing,by(treat1)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       0 |   2,275    .0874725    .0059247    .2825884    .0758542    .0990908
       1 |   2,284     .058669    .0049184    .2350556     .049024     .068314
---------+--------------------------------------------------------------------
Combined |   4,559    .0730423    .0038542    .2602345    .0654863    .0805984
---------+--------------------------------------------------------------------
    diff |            .0288035    .0076974                .0137129    .0438941
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   3.7420
H0: diff = 0                                     Degrees of freedom =     4557

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9999         Pr(|T| > |t|) = 0.0002          Pr(T > t) = 0.0001

. 
.                                          * Comparison margins estimate from RE
>  probit *
.                                          global cvarlpm="lnregion lt lxyrs"

.                                          xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                                          qui xtprobit xonset period* $cvarlpm 
> treat1,  vce(cluster gwf_caseid)

.                                          margins,dydx(treat1)

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(xonset=1), predict(pr)
dy/dx wrt:  treat1

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      treat1 |  -.0234335   .0090666    -2.58   0.010    -.0412037   -.0056633
------------------------------------------------------------------------------

.                                                         
.                                          * 2-way FE *
.                                          qui reghdfe xonset $cvarlpm treat1,a(
> gwf_caseid year)vce(cluster gwf_leaderid)

.                                          lincom treat1

 ( 1)  treat1 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0247662   .0092822    -2.67   0.008    -.0430051   -.0065274
------------------------------------------------------------------------------

.                                          est store lpm1

. 
.                                          * Unit-specific quadratic time trend 
> *
.                                          set matsize 10000
set matsize ignored.
    Matrix sizes are no longer limited by c(matsize) in modern Statas.  Matrix
    sizes are now limited by edition of Stata.  See limits for more details.

.                                          xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                                          xi:qui reg xonset i.gwf_caseid*time i
> .gwf_caseid*time2 ///
>                                                 lxyrs lt lnregion treat1,vce(c
> luster gwf_leaderid)
i.gwf_caseid      _Igwf_casei_1-280   (naturally coded; _Igwf_casei_1 omitted)
i.gwf_ca~d*time   _IgwfXtim_#         (coded as above)
i.gwf_c~d*time2   _IgwfXtima#         (coded as above)

.                                          lincom treat1

 ( 1)  treat1 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0334897    .016442    -2.04   0.042    -.0657931   -.0011863
------------------------------------------------------------------------------

.                                          est store lpm2

.                                          
.                                          * Interactive FE *
.                                          qui regife xonset $cvarlpm treat1,a(g
> wf_caseid year)factor(gwf_caseid year,1) vce(cluster gwf_leaderid)

.                                          lincom treat1

 ( 1)  treat1 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0210222   .0094151    -2.23   0.026    -.0395221   -.0025223
------------------------------------------------------------------------------

.                                          est store lpm3

.                                          
.                                          * Past treatment *
.                                          reghdfe xonset $cvarlpm treat1 l1trea
> t1 l2treat1,a(gwf_caseid year)cluster(gwf_leaderid)
(dropped 16 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =      4,007
Absorbing 2 HDFE groups                           F(   6,    419) =       5.47
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1417
                                                  Adj R-squared   =     0.0760
                                                  Within R-sq.    =     0.0136
Number of clusters (gwf_leaderid) =        420    Root MSE        =     0.1888

                         (Std. err. adjusted for 420 clusters in gwf_leaderid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    lnregion |   .0143423   .0042153     3.40   0.001     .0060564    .0226282
          lt |   .0082233   .0048322     1.70   0.090    -.0012749    .0177216
       lxyrs |   .0250632   .0072711     3.45   0.001     .0107709    .0393555
      treat1 |  -.0435864    .018839    -2.31   0.021     -.080617   -.0065557
    l1treat1 |   .0001694   .0170682     0.01   0.992    -.0333806    .0337194
    l2treat1 |   .0257794   .0143772     1.79   0.074     -.002481    .0540398
       _cons |   .0446298   .0068902     6.48   0.000     .0310862    .0581734
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  gwf_caseid |       218           0         218     |
        year |        63           1          62     |
-----------------------------------------------------+

.                                          est store lpm4

.                                          lincom treat1

 ( 1)  treat1 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0435864    .018839    -2.31   0.021     -.080617   -.0065557
------------------------------------------------------------------------------

.                                          test l1treat1 l2treat1  

 ( 1)  l1treat1 = 0
 ( 2)  l2treat1 = 0

       F(  2,   419) =    1.94
            Prob > F =    0.1453

. 
.                                          * Past outcome *
.                                          reghdfe xonset $cvarlpm treat1 l1xons
> et l2xonset,a(gwf_caseid year)cluster(gwf_leaderid)
(dropped 16 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =      4,007
Absorbing 2 HDFE groups                           F(   6,    419) =       5.14
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1419
                                                  Adj R-squared   =     0.0762
                                                  Within R-sq.    =     0.0138
Number of clusters (gwf_leaderid) =        420    Root MSE        =     0.1888

                         (Std. err. adjusted for 420 clusters in gwf_leaderid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    lnregion |   .0147246   .0042869     3.43   0.001     .0062981    .0231511
          lt |     .00887    .004923     1.80   0.072    -.0008069    .0185468
       lxyrs |   .0161271   .0075319     2.14   0.033     .0013221    .0309322
      treat1 |  -.0255211   .0112018    -2.28   0.023    -.0475399   -.0035023
    l1xonset |  -.0491869   .0308895    -1.59   0.112    -.1099046    .0115308
    l2xonset |  -.0141041   .0317482    -0.44   0.657    -.0765096    .0483014
       _cons |   .0513522   .0066874     7.68   0.000     .0382072    .0644972
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  gwf_caseid |       218           0         218     |
        year |        63           1          62     |
-----------------------------------------------------+

.                                          est store lpm5

.                                          lincom treat1

 ( 1)  treat1 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0255211   .0112018    -2.28   0.023    -.0475399   -.0035023
------------------------------------------------------------------------------

.                                          test l1xonset l2xonset  

 ( 1)  l1xonset = 0
 ( 2)  l2xonset = 0

       F(  2,   419) =    1.33
            Prob > F =    0.2659

.                                          
.                                          * Past outcome, low level *
.                                          reghdfe xonset $cvarlpm treat1 l1v2ca
> demmob l2v2cademmob,a(gwf_caseid year)cluster(gwf_leaderid)
(dropped 18 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =      3,155
Absorbing 2 HDFE groups                           F(   6,    317) =       7.43
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1650
                                                  Adj R-squared   =     0.0971
                                                  Within R-sq.    =     0.0288
Number of clusters (gwf_leaderid) =        318    Root MSE        =     0.1896

                         (Std. err. adjusted for 318 clusters in gwf_leaderid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    lnregion |    .011108   .0048834     2.27   0.024        .0015     .020716
          lt |   .0139681   .0048903     2.86   0.005     .0043465    .0235897
       lxyrs |   .0329621   .0088861     3.71   0.000      .015479    .0504453
      treat1 |  -.0310566   .0111483    -2.79   0.006    -.0529907   -.0091226
l1v2cademmob |   .0523268   .0117344     4.46   0.000     .0292397     .075414
l2v2cademmob |  -.0221696   .0093974    -2.36   0.019    -.0406588   -.0036804
       _cons |   .0767178     .00914     8.39   0.000     .0587352    .0947005
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  gwf_caseid |       168           0         168     |
        year |        65           1          64     |
-----------------------------------------------------+

.                                          est store lpm6

.                                          * Check with 4 lags, per Hamilton 201
> 8 RES
.                                          reghdfe xonset $cvarlpm treat1 l1v2ca
> demmob l2v2cademmob l3v2cademmob,a(gwf_caseid year)cluster(gwf_leaderid)
(dropped 15 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =      3,139
Absorbing 2 HDFE groups                           F(   7,    317) =       6.65
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1654
                                                  Adj R-squared   =     0.0969
                                                  Within R-sq.    =     0.0291
Number of clusters (gwf_leaderid) =        318    Root MSE        =     0.1901

                         (Std. err. adjusted for 318 clusters in gwf_leaderid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    lnregion |   .0111191   .0049072     2.27   0.024     .0014643    .0207738
          lt |   .0141029   .0049516     2.85   0.005     .0043608    .0238449
       lxyrs |   .0336745   .0090432     3.72   0.000     .0158823    .0514668
      treat1 |  -.0306686   .0112171    -2.73   0.007     -.052738   -.0085992
l1v2cademmob |   .0527579   .0118322     4.46   0.000     .0294784    .0760375
l2v2cademmob |  -.0214984   .0112997    -1.90   0.058    -.0437304    .0007335
l3v2cademmob |  -.0010802   .0066703    -0.16   0.871    -.0142039    .0120434
       _cons |   .0763807   .0091602     8.34   0.000     .0583583    .0944031
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  gwf_caseid |       168           0         168     |
        year |        65           1          64     |
-----------------------------------------------------+

.                                          reghdfe xonset $cvarlpm treat1 l1v2ca
> demmob l2v2cademmob l3v2cademmob l4v2cademmob,a(gwf_caseid year)cluster(gwf_le
> aderid)
(dropped 17 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =      3,120
Absorbing 2 HDFE groups                           F(   8,    315) =       5.84
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1661
                                                  Adj R-squared   =     0.0975
                                                  Within R-sq.    =     0.0295
Number of clusters (gwf_leaderid) =        316    Root MSE        =     0.1906

                         (Std. err. adjusted for 316 clusters in gwf_leaderid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    lnregion |   .0111192   .0049186     2.26   0.024     .0014417    .0207967
          lt |   .0139896   .0049672     2.82   0.005     .0042164    .0237628
       lxyrs |   .0344528   .0090782     3.80   0.000     .0165912    .0523143
      treat1 |  -.0297277   .0113108    -2.63   0.009    -.0519819   -.0074735
l1v2cademmob |   .0534842   .0119394     4.48   0.000     .0299931    .0769753
l2v2cademmob |  -.0218382   .0114463    -1.91   0.057     -.044359    .0006827
l3v2cademmob |  -.0018819   .0079191    -0.24   0.812    -.0174628     .013699
l4v2cademmob |   .0015215   .0065478     0.23   0.816    -.0113614    .0144044
       _cons |   .0766347   .0095196     8.05   0.000     .0579046    .0953647
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  gwf_caseid |       166           0         166     |
        year |        65           1          64     |
-----------------------------------------------------+

. 
.                                          * Check with continous *
.                                          reghdfe xonset $cvarlpm xpers2 l1v2ca
> demmob l2v2cademmob l3v2cademmob,a(gwf_caseid year)cluster(gwf_leaderid)
(dropped 15 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =      3,139
Absorbing 2 HDFE groups                           F(   7,    317) =       6.66
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1646
                                                  Adj R-squared   =     0.0960
                                                  Within R-sq.    =     0.0281
Number of clusters (gwf_leaderid) =        318    Root MSE        =     0.1902

                         (Std. err. adjusted for 318 clusters in gwf_leaderid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    lnregion |   .0110933   .0049093     2.26   0.025     .0014343    .0207523
          lt |   .0136292   .0049056     2.78   0.006     .0039775    .0232809
       lxyrs |   .0334112   .0089547     3.73   0.000      .015793    .0510294
      xpers2 |  -.0118287   .0070702    -1.67   0.095    -.0257391    .0020817
l1v2cademmob |    .053085   .0119782     4.43   0.000     .0295183    .0766518
l2v2cademmob |  -.0214312    .011325    -1.89   0.059    -.0437128    .0008504
l3v2cademmob |  -.0012207   .0066704    -0.18   0.855    -.0143445     .011903
       _cons |     .06082   .0054961    11.07   0.000     .0500064    .0716335
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  gwf_caseid |       168           0         168     |
        year |        65           1          64     |
-----------------------------------------------------+

.                                          reghdfe v2cademmob $cvarlpm treat1 l1
> v2cademmob l2v2cademmob l3v2cademmob,a(gwf_caseid year)cluster(gwf_leaderid)
(dropped 15 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =      3,139
Absorbing 2 HDFE groups                           F(   7,    317) =     110.69
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.8748
                                                  Adj R-squared   =     0.8646
                                                  Within R-sq.    =     0.4756
Number of clusters (gwf_leaderid) =        318    Root MSE        =     0.5147

                         (Std. err. adjusted for 318 clusters in gwf_leaderid)
------------------------------------------------------------------------------
             |               Robust
  v2cademmob | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    lnregion |   .0428285   .0146478     2.92   0.004     .0140094    .0716476
          lt |   .0573244   .0186599     3.07   0.002     .0206116    .0940373
       lxyrs |  -.0329928   .0308204    -1.07   0.285    -.0936313    .0276457
      treat1 |  -.1158369   .0585936    -1.98   0.049    -.2311184   -.0005554
l1v2cademmob |    .703951   .0374453    18.80   0.000     .6302782    .7776237
l2v2cademmob |   .0071547    .032741     0.22   0.827    -.0572624    .0715717
l3v2cademmob |   .0028267   .0228301     0.12   0.902     -.042091    .0477444
       _cons |  -.1149088   .0371134    -3.10   0.002    -.1879284   -.0418892
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  gwf_caseid |       168           0         168     |
        year |        65           1          64     |
-----------------------------------------------------+

.                                         
.                                          * Past outcome and past treatment *
.                                                 * First test whether t-2 and t
> -3 lags of outcome/treatment are related to outcome conditional on 1-year lags
>  *
.                                          reghdfe xonset $cvarlpm treat1 l2xons
> et l3xonset l2treat1 l3treat1,a(gwf_caseid year) cluster(gwf_leaderid)
(dropped 12 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =      3,777
Absorbing 2 HDFE groups                           F(   8,    392) =       4.30
Statistics robust to heteroskedasticity           Prob > F        =     0.0001
                                                  R-squared       =     0.1546
                                                  Adj R-squared   =     0.0885
                                                  Within R-sq.    =     0.0154
Number of clusters (gwf_leaderid) =        393    Root MSE        =     0.1912

                         (Std. err. adjusted for 393 clusters in gwf_leaderid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    lnregion |   .0145319   .0044446     3.27   0.001     .0057937    .0232701
          lt |   .0085622   .0050578     1.69   0.091    -.0013816     .018506
       lxyrs |   .0283668   .0074084     3.83   0.000     .0138017    .0429319
      treat1 |  -.0508246   .0155577    -3.27   0.001    -.0814116   -.0202375
    l2xonset |  -.0051979   .0275991    -0.19   0.851    -.0594587    .0490629
    l3xonset |   .0209348   .0315131     0.66   0.507    -.0410211    .0828907
    l2treat1 |   .0241551   .0169869     1.42   0.156    -.0092417    .0575519
    l3treat1 |   .0051163   .0162912     0.31   0.754    -.0269128    .0371453
       _cons |   .0455451   .0082115     5.55   0.000      .029401    .0616891
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  gwf_caseid |       206           0         206     |
        year |        62           1          61     |
-----------------------------------------------------+

.                                          test l2xonset l3xonset l2treat1 l3tre
> at1

 ( 1)  l2xonset = 0
 ( 2)  l3xonset = 0
 ( 3)  l2treat1 = 0
 ( 4)  l3treat1 = 0

       F(  4,   392) =    1.14
            Prob > F =    0.3383

.                                          reghdfe xonset $cvarlpm treat1 l1trea
> t l1xonset l2xonset l3xonset l2treat1 l3treat1,a(gwf_caseid year) cluster(gwf_
> leaderid)
(dropped 12 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =      3,777
Absorbing 2 HDFE groups                           F(  10,    392) =       3.50
Statistics robust to heteroskedasticity           Prob > F        =     0.0002
                                                  R-squared       =     0.1555
                                                  Adj R-squared   =     0.0889
                                                  Within R-sq.    =     0.0164
Number of clusters (gwf_leaderid) =        393    Root MSE        =     0.1911

                         (Std. err. adjusted for 393 clusters in gwf_leaderid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    lnregion |   .0147282   .0044815     3.29   0.001     .0059173    .0235391
          lt |   .0091499   .0051119     1.79   0.074    -.0009004    .0192001
       lxyrs |   .0172529   .0084589     2.04   0.042     .0006225    .0338834
      treat1 |  -.0542409   .0199675    -2.72   0.007    -.0934976   -.0149842
    l1treat1 |   .0068723   .0195268     0.35   0.725    -.0315181    .0452628
    l1xonset |  -.0506173   .0341595    -1.48   0.139    -.1177761    .0165414
    l2xonset |  -.0267407   .0329822    -0.81   0.418    -.0915848    .0381034
    l3xonset |   .0064143   .0339994     0.19   0.850    -.0604296    .0732583
    l2treat1 |   .0211661   .0195973     1.08   0.281    -.0173629    .0596951
    l3treat1 |   .0066707   .0164649     0.41   0.686    -.0256998    .0390412
       _cons |   .0488855   .0082901     5.90   0.000     .0325868    .0651842
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  gwf_caseid |       206           0         206     |
        year |        62           1          61     |
-----------------------------------------------------+

.                                          test l2xonset l3xonset l2treat1 l3tre
> at1

 ( 1)  l2xonset = 0
 ( 2)  l3xonset = 0
 ( 3)  l2treat1 = 0
 ( 4)  l3treat1 = 0

       F(  4,   392) =    0.74
            Prob > F =    0.5661

.                                          ivreghdfe xonset $cvarlpm treat1 (l1t
> reat1 l1xonset= l2xonset l3xonset l2treat1 l3treat1),a(gwf_caseid year) cluste
> r(gwf_leaderid)
(dropped 12 singleton observations)
(MWFE estimator converged in 10 iterations)

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on gwf_leaderid

Number of clusters (gwf_leaderid) =    393            Number of obs =     3777
                                                      F(  6,   392) =     5.80
                                                      Prob > F      =   0.0000
Total (centered) SS     =  129.9868852                Centered R2   =   0.0143
Total (uncentered) SS   =  129.9868852                Uncentered R2 =   0.0143
Residual SS             =  128.1323978                Root MSE      =    .1912

------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    l1treat1 |   .0589836   .0301179     1.96   0.051    -.0002293    .1181965
    l1xonset |  -.0070167   .0578683    -0.12   0.904    -.1207877    .1067543
    lnregion |   .0145223   .0044457     3.27   0.001     .0057818    .0232628
          lt |   .0091352   .0050335     1.81   0.070    -.0007608    .0190312
       lxyrs |   .0266781    .013641     1.96   0.051    -.0001406    .0534968
      treat1 |  -.0799423    .027488    -2.91   0.004    -.1339847   -.0258999
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):             73.702
                                                   Chi-sq(3) P-val =    0.0000
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):              298.427
                         (Kleibergen-Paap rk Wald F statistic):         98.597
Stock-Yogo weak ID test critical values:  5% maximal IV relative bias    11.04
                                         10% maximal IV relative bias     7.56
                                         20% maximal IV relative bias     5.57
                                         30% maximal IV relative bias     4.73
                                         10% maximal IV size             16.87
                                         15% maximal IV size              9.93
                                         20% maximal IV size              7.54
                                         25% maximal IV size              6.28
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.672
                                                   Chi-sq(2) P-val =    0.7146
------------------------------------------------------------------------------
Instrumented:         l1treat1 l1xonset
Included instruments: lnregion lt lxyrs treat1
Excluded instruments: l2xonset l3xonset l2treat1 l3treat1
Partialled-out:       _cons
                      nb: total SS, model F and R2s are after partialling-out;
                          any small-sample adjustments include partialled-out
                          variables in regressor count K
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  gwf_caseid |       206           0         206     |
        year |        62           1          61     |
-----------------------------------------------------+

.                                          lincom treat1

 ( 1)  treat1 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0799423    .027488    -2.91   0.004    -.1339847   -.0258999
------------------------------------------------------------------------------

.                                          est store lpm7

.                                          
.                                          * Check with continous treatment *
.                                          ivreghdfe xonset $cvarlpm xpers2 (l1.
> xpers2 l1xonset= l2xonset l3xonset l2.xpers2 l3.xpers2), ///
>                                                 a(gwf_caseid year) cluster(gwf
> _leaderid)
(dropped 12 singleton observations)
(MWFE estimator converged in 10 iterations)

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on gwf_leaderid

Number of clusters (gwf_leaderid) =    393            Number of obs =     3777
                                                      F(  6,   392) =     4.83
                                                      Prob > F      =   0.0001
Total (centered) SS     =  129.9868852                Centered R2   =   0.0143
Total (uncentered) SS   =  129.9868852                Uncentered R2 =   0.0143
Residual SS             =   128.127035                Root MSE      =    .1912

------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |
         L1. |    .039948   .0213125     1.87   0.062    -.0019532    .0818491
             |
    l1xonset |  -.0101563   .0571833    -0.18   0.859    -.1225806     .102268
    lnregion |   .0146495   .0044484     3.29   0.001     .0059037    .0233953
          lt |   .0084281   .0048772     1.73   0.085    -.0011606    .0180168
       lxyrs |   .0262247   .0135935     1.93   0.054    -.0005005      .05295
      xpers2 |  -.0431416   .0192583    -2.24   0.026    -.0810041   -.0052791
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):             73.748
                                                   Chi-sq(3) P-val =    0.0000
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):              300.094
                         (Kleibergen-Paap rk Wald F statistic):         96.452
Stock-Yogo weak ID test critical values:  5% maximal IV relative bias    11.04
                                         10% maximal IV relative bias     7.56
                                         20% maximal IV relative bias     5.57
                                         30% maximal IV relative bias     4.73
                                         10% maximal IV size             16.87
                                         15% maximal IV size              9.93
                                         20% maximal IV size              7.54
                                         25% maximal IV size              6.28
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.648
                                                   Chi-sq(2) P-val =    0.7232
------------------------------------------------------------------------------
Instrumented:         L.xpers2 l1xonset
Included instruments: lnregion lt lxyrs xpers2
Excluded instruments: l2xonset l3xonset L2.xpers2 L3.xpers2
Partialled-out:       _cons
                      nb: total SS, model F and R2s are after partialling-out;
                          any small-sample adjustments include partialled-out
                          variables in regressor count K
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  gwf_caseid |       206           0         206     |
        year |        62           1          61     |
-----------------------------------------------------+

.                                          
.                                         * LPM table *
.                                         estout lpm* using TableB1.tex, cells(b
> (star  fmt(%9.3f)) se(par fmt(%9.2f))) ///
>                                                 stats(r2 N N_clust widstat) st
> yle(tex) replace label starlevels(* 0.05) title(\label{tabB1})            
(file TableB1.tex not found)
(output written to TableB1.tex)

.                                         saveold temp-fe1,replace version(12)
(saving in Stata 12 format, which can be read by Stata 11 or 12)
(file temp-fe1.dta not found)
file temp-fe1.dta saved

. 
.                                                 /*  R script for weight-FE (WF
> E) estimator: 
>                                                 ls()
>                                                 setwd("C:/Users/jgw12/Dropbox/
> Research/Pers-NAVCO/Pers-RENAVCO/CSW-BJPS-reproduction")
>                                                 getwd()
> 
>                                                 rm(list=ls())
>                                                 set.seed(23789)
> 
>                                                 ## install missing packages, a
> nd update if newer version available
> 
>                                                 packageList<-c("car","plm","xt
> able","wfe","sandwich","stargazer")
> 
>                                                 for(i in 1:length(packageList)
> ){
>                                                         if (!require(packageLi
> st[i],character.only = TRUE)){
>                                                                 install.packag
> es(packageList[i], repos="http://lib.stat.cmu.edu/R/CRAN/")
>                                                         }
>                                                 }
>                                                 # update.packages(ask = FALSE,
>  dependencies = c('Suggests'), oldPkgs=packageList, repos="http://lib.stat.cmu
> .edu/R/CRAN/")
>                                                  
>                                                 library(car)
>                                                 library(foreign)
>                                                 library(xtable)
>                                                 library(wfe)
>                                                 library(sandwich)
>                                                 library(stats4)
>                                                  
> 
>                                                 rm(list=ls())
>                                                 set.seed(89270258)
>                                                 data.temp<-read.dta("temp-fe1.
> dta")
> 
>                                                 # Unweighted unit FE + period 
> effects #
>                                                 mod.W1<-wfe(xonset~treat1+lxyr
> s+lnregion+lt+
>                                                                           peri
> od1+period2+period3+period4+period5+
>                                                                           peri
> od6+period7+period8+period9+period10+
>                                                                           peri
> od11+period12,
>                                                                          data=
> data.temp,treat="treat1",
>                                                                          unit.
> index="gwf_caseid",time.index="year",
>                                                                          metho
> d="unit", qoi="ate",hetero.se=TRUE,
>                                                                          auto.
> se=TRUE,unweighted=TRUE)
>                                                  summary(mod.W1)
>                                                  
>                                                  # Weighted unit FE + period e
> ffects #
>                                                  mod.W2<-wfe(xonset~treat1+lxy
> rs+lnregion+lt+
>                                                                            per
> iod1+period2+period3+period4+period5+
>                                                                            per
> iod6+period7+period8+period9+period10+
>                                                                            per
> iod11+period12,
>                                                                           data
> =data.temp,treat="treat1",
>                                                                           unit
> .index="gwf_caseid",time.index="year", 
>                                                                          metho
> d="unit",qoi="ate",hetero.se=TRUE,
>                                                                           auto
> .se=TRUE,unweighted=FALSE)
>                                                 summary(mod.W2)
>                                                  
> 
>                                                 # Two-way FE UNweighted #
>                                                 mod.W3<-wfe(xonset~treat1+lxyr
> s+lnregion+lt,
>                                                                         data=d
> ata.temp,treat="treat1",
>                                                                         unit.i
> ndex="gwf_caseid",
>                                                                         time.i
> ndex="year", method="unit",
>                                                                         qoi="a
> te",hetero.se=TRUE,
>                                                                         auto.s
> e=TRUE,unweighted = TRUE,
>                                                                         estima
> tor = "did")
>                                                 summary(mod.W3)
> 
>                                                 # Two-way FE Weighted #
>                                                 mod.W4<-wfe(xonset~treat1+lxyr
> s+lnregion+lt,
>                                                                         data=d
> ata.temp,treat="treat1",
>                                                                         unit.i
> ndex="gwf_caseid",
>                                                                         time.i
> ndex="year", method="unit",
>                                                                         qoi="a
> te",hetero.se=TRUE,
>                                                                         auto.s
> e=TRUE,unweighted =FALSE,
>                                                                         estima
> tor = "did")
>                                                 summary(mod.W4)
> 
>                                                 */
.                                                 
.                         * Semiparametric estimator *
.                         use temp-fe,clear

.                         xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                         set seed 897243574

.                         xi:qui xtsemipar xonset i.year xpers2 lt lxyrs lnregio
> n,nonpar(xpers2)cluster(gwf_leaderid)deg(3)gen(a b)spline
i.year            _Iyear_1946-2010    (naturally coded; _Iyear_1946 omitted)

.                         qui gen invb= invlogit(b) /* rescale on 0,1 */

.                         qui sum invb if sample==1

.                         di r(mean)
.4994319

.                         qui sum xonset   if sample==1 & invb~=.

.                         replace invb = invb-(.49943119-r(mean)) if sample==1 
(4,535 real changes made)

.                         sum invb xonset if sample==1 & invb~=.

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
        invb |      4,535    .0390305    .0671187  -.1720866   .4167717
      xonset |      4,535    .0390298    .1936872          0          1

.                         qui gen inva=invlogit(a)

.                         qui sum inva if sample==1

.                         di r(mean)
.49830352

.                         qui sum xonset if sample==1 & inva~=.

.                         replace inva = inva-(.4983158-r(mean)) if sample==1 
(4,279 real changes made)

.                         twoway (hist xpers2,bin(50)bcol(gs12)yscale(range(0 30
> 00)axis(2))yaxis(2) ///   
>                                 ylab(none,axis(2))freq ytitle("",axis(2)))(lpo
> lyci invb xpers2,bw(.5) xtit(Security personalism)ylab(.03(.01).06) yscale(alt
> ) ///
>                                 legend(off) ytitle(Probability of campaign ons
> et)col(blue)xlab(-1.5(.5)1.5)yline(.0390294)) 

.                         graph export "$dir\onset-semipar.pdf", as(pdf) replace
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\onset-semipar.pdf saved as PDF format

.                         drop a b invb inva

.                         
.                         * Time trend models coefficient plot *
.                         coefplot (time1, msym(d)) (time2, msym(t)) (time3, msy
> m(oh)) (time4, msym(plus)) (time5, msym(P)), ///
>                                 title("Adjusting for time trends", size(small)
> ) drop(_cons xyr* lxyr* time* d* coldwar period*) ///
>                                 order(xpers2) xline(0) grid(glcolor(gs13)) mfc
> olor(white) xlabel(-.5(.25).5,labsize(vsmall))  levels(95 90)  ///
>                                 legend(lab(3 "1946-1988" "period") lab(6 "1989
> -2010" "period") lab(9 "Five-year"  "periods") lab(12 "Non-linear"  "time tren
> d")  ///
>                                 lab(15 "Decade"  "dummies") size(vsmall)) ysiz
> e(3) xsize(3.5) saving(r1, replace)       ///
>                                 xtitle("Coefficient estimate", size(small) hei
> ght(6))note("90 (thick) and 95 (thin) percent confidence intervals", size(vsma
> ll) pos(6))
(note:  named style P not found in class symbol, default attributes used)
file r1.gph saved

.                                 graph export "$dir\time-models.pdf", as(pdf) r
> eplace
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\time-models.pdf saved as PDF format

.                                                 
.                         * Drop each region *
.                           local region = "cacar casia ceeurope easia meast naf
> rica samerica ssafrica weu"

.                           local i =1

.                           foreach v of local region {
  2.                                 qui xtprobit xonset lxyrs lt lnregion xpers
> 2 if region~="`v'",re vce(cluster gwf_caseid)
  3.                                 lincom xpers2
  4.                                 est store region`i'
  5.                                 local i =`i' +1
  6.                           }

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1895366   .0510303    -3.71   0.000    -.2895542    -.089519
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   -.151745   .0490857    -3.09   0.002    -.2479511   -.0555388
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1118226   .0546236    -2.05   0.041    -.2188829   -.0047623
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1382936    .047886    -2.89   0.004    -.2321483   -.0444388
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1291143    .045853    -2.82   0.005    -.2189846   -.0392441
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1439228   .0479967    -3.00   0.003    -.2379946   -.0498511
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1365063   .0491511    -2.78   0.005    -.2328407    -.040172
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1754009   .0567474    -3.09   0.002    -.2866237    -.064178
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1379372   .0465003    -2.97   0.003    -.2290762   -.0467983
------------------------------------------------------------------------------

.                           
.                         * Drop each decade *
.                           qui xtprobit xonset lxyrs lt lnregion xpers2 if year
> >=1960,re vce(cluster gwf_caseid)

.                           lincom xpers2

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1382696   .0476299    -2.90   0.004    -.2316226   -.0449166
------------------------------------------------------------------------------

.                           est store decade1

.                           local decade = "1960 1970 1980 1990 2000"

.                           local i =2

.                           foreach v of local decade {
  2.                                 qui xtprobit xonset lxyrs lt lnregion xpers
> 2 if d`v'==0,re vce(cluster gwf_caseid)
  3.                                 lincom xpers2
  4.                                 est store decade`i'
  5.                                 local i =`i' +1
  6.                           }

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1186446   .0483811    -2.45   0.014    -.2134699   -.0238193
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1596414   .0517009    -3.09   0.002    -.2609733   -.0583094
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    -.14874   .0553265    -2.69   0.007     -.257178   -.0403021
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1342003   .0495114    -2.71   0.007     -.231241   -.0371597
------------------------------------------------------------------------------

 ( 1)  [xonset]xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1542828   .0485508    -3.18   0.001    -.2494406   -.0591249
------------------------------------------------------------------------------

.                           
.                         label var xpers "Personalization"

.                         label var xpers1 `""Party        " "personalization""'

.                         label var xpers2 `""Security    " "personalization""'

.                         label var lpopl "Population (log)"

.                         label var lt `""Leader" "tenure (log)""'

.                         label var lnregion `""Region NVC" "onsets (log)""'

.                         label var gwf_pers `""Personalist" "regime    ""'

.                           
.                         * Drop regions models coefficient plot *
.                         coefplot (region1, msym(d)) (region2, msym(t)) (region
> 3, msym(oh)) (region4, msym(plus)) (region5, msym(P)) ///
>                                 (region6, msym(d)) (region7, msym(t)) (region8
> , msym(oh)) (region9, msym(plus)) , ///
>                                 title("Drop one region at a time", size(small)
> ) drop(_cons pyr* time* d* coldwar) ///
>                                 order(xpers2) xline(0) grid(glcolor(gs13)) mfc
> olor(white) xlabel(-.3(.15).3,labsize(vsmall))  levels(95 90)  ///
>                                 legend(lab(3 "Central" "America") lab(6 "Centr
> al" "Asia") lab(9 "Central/East"  "Europe") lab(12 "East"  "Asia") ///
>                                 lab(15 "Middle"  "East") lab(18 "North"  "Afri
> ca") lab(21 "South"  "America") lab(24 "Sub-Saharan"  "Africa") ///
>                                 lab(27 "Western"  "Europe") size(vsmall)) ysiz
> e(3) xsize(3.5) saving(r1, replace)       ///
>                                 xtitle("Coefficient estimate", size(small) hei
> ght(6))note("90 (thick) and 95 (thin) percent confidence intervals", ///
>                                 size(vsmall) pos(6))
(note:  named style P not found in class symbol, default attributes used)
file r1.gph saved

.                                 graph export "$dir\drop-region-models.pdf", as
> (pdf) replace
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\drop-region-models.pdf saved as PDF format

.                                 
.                         * Drop decade models coefficient plot *
.                         coefplot (decade1, msym(d)) (decade2, msym(t)) (decade
> 3, msym(oh)) (decade4, msym(plus)) (decade5, msym(P)) (decade6, msym(d)), ///
>                                 title("Drop each decade at a time", size(small
> )) drop(_cons pyr*) ///
>                                 order(xpers2) xline(0) grid(glcolor(gs13)) mfc
> olor(white) xlabel(-.3(.15).3,labsize(vsmall))  levels(95 90)  ///
>                                 legend(lab(3 "1940s" "1950s") lab(6 "1960s") l
> ab(9 "1970s") lab(12 "1980s") ///
>                                 lab(15 "1990s") lab(18 "2000s") size(vsmall)) 
> ysize(3) xsize(3.5) saving(r1, replace) xtitle("Coefficient estimate", ///
>                                 size(small) height(6))note("90 (thick) and 95 
> (thin) percent confidence intervals", size(vsmall) pos(6))
(note:  named style P not found in class symbol, default attributes used)
file r1.gph saved

.                                 graph export "$dir\drop-decade-models.pdf", as
> (pdf) replace
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\drop-decade-models.pdf saved as PDF format

.                                 
.                         * Alternative DV models coefficient plot *
.                         coefplot (alt0, msym(d)) (alt1, msym(t)) (alt2, msym(o
> h)) (alt3, msym(plus)) (alt4, msym(S)), ///
>                                 title("Security personalization and Non-violen
> t protest" "campaign onsets, alternative DV tests", ///
>                                 size(small)) drop(_cons xyrs* yrs* lyrs* perio
> d*) order(xpers2 lt lpopl lnregion) ///
>                                 xline(0) grid(glcolor(gs13)) mfcolor(white) xl
> abel(-.3(.15).3,labsize(vsmall)) levels(95 90) ///
>                                 legend(lab(3 "Original") lab(6 "MEC") lab(9 "N
> AVCO 1.3") lab(12 "NAVCO 2.1") lab(15 "NEVER") size(vsmall)) ///
>                                 ysize(3) xsize(3.5) xtitle("Coefficient estima
> te", size(small) height(6))  ///
>                                 note("90 (thick) and 95 (thin) percent confide
> nce intervals", size(vsmall) pos(6))

.                                 graph export "$dir\alt-models.pdf", as(pdf)   
> replace
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\alt-models.pdf saved as PDF format

.                                 
.                 *** Cox model with shared frailty ***
.                         use temp-fe,clear

.                         gen oxyrs = xyrs+1

.                         sum oxyrs

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       oxyrs |      4,559    16.82562    13.87821          1         65

.                         stset oxyrs, fail(xonset)

Survival-time data settings

         Failure event: xonset!=0 & xonset<.
Observed time interval: (0, oxyrs]
     Exit on or before: failure

--------------------------------------------------------------------------
      4,559  total observations
          0  exclusions
--------------------------------------------------------------------------
      4,559  observations remaining, representing
        182  failures in single-record/single-failure data
     76,708  total analysis time at risk and under observation
                                                At risk from t =         0
                                     Earliest observed entry t =         0
                                          Last observed exit t =        65

.                         stcox coldwar lt lnregion xpers2 lpop,shared(gwf_casei
> d) nohr

        Failure _d: xonset
  Analysis time _t: oxyrs

Fitting comparison Cox model ...

Estimating frailty variance:
Iteration 0:   log profile likelihood = -1209.2262  
Iteration 1:   log profile likelihood = -1209.1281  
Iteration 2:   log profile likelihood =  -1209.128  

Fitting final Cox model:
Iteration 0:   log likelihood =  -1542.799
Iteration 1:   log likelihood = -1395.1861
Iteration 2:   log likelihood = -1256.2784
Iteration 3:   log likelihood = -1218.8568
Iteration 4:   log likelihood = -1210.6061
Iteration 5:   log likelihood = -1209.2615
Iteration 6:   log likelihood =  -1209.131
Iteration 7:   log likelihood =  -1209.128
Iteration 8:   log likelihood =  -1209.128
Refining estimates:
Iteration 0:   log likelihood =  -1209.128

Cox regression with Breslow method for ties
Gamma shared frailty                                Number of obs     =  4,510
Group variable: gwf_caseid                          Number of groups  =    279
                                                    Obs per group:   
No. of subjects =  4,510                                          min =      1
No. of failures =    182                                          avg =     16
Time at risk    = 75,603                                          max =     65
                                                    Wald chi2(5)      =  51.31
Log likelihood = -1209.128                          Prob > chi2       = 0.0000

------------------------------------------------------------------------------
          _t | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     coldwar |  -1.323255   .2845757    -4.65   0.000    -1.881013   -.7654964
          lt |   .0676071   .1255168     0.54   0.590    -.1784013    .3136155
    lnregion |   .2234408   .0769107     2.91   0.004     .0726985     .374183
      xpers2 |   -.298359   .1634511    -1.83   0.068    -.6187172    .0219992
       lpopl |   .3441075   .2019909     1.70   0.088    -.0517873    .7400024
-------------+----------------------------------------------------------------
       theta |   6.899709   1.068683
------------------------------------------------------------------------------
LR test of theta=0: chibar2(01) = 236.36               Prob >= chibar2 = 0.000

Note: Standard errors of regression parameters are conditional on theta.

.                         estat phtest,detail

Test of proportional-hazards assumption

Time function: Analysis time
--------------------------------------------------------
             |        rho     chi2       df    Prob>chi2
-------------+------------------------------------------
     coldwar |    0.01570     0.15        1       0.6954
          lt |    0.13831     6.88        1       0.0087
    lnregion |    0.00202     0.00        1       0.9755
      xpers2 |    0.00549     0.02        1       0.8842
       lpopl |    0.02012     1.07        1       0.3002
-------------+------------------------------------------
 Global test |                9.25        5       0.0994
--------------------------------------------------------

.                         gen lnoxyrs = ln(oxyrs)

.                         gen stXlt = lnoxyrs*lt

.                         stcox lt coldwar lnregion xpers2 lpop stXlt,shared(gwf
> _caseid) nohr

        Failure _d: xonset
  Analysis time _t: oxyrs

Fitting comparison Cox model ...

Estimating frailty variance:
Iteration 0:   log profile likelihood = -1203.6345  
Iteration 1:   log profile likelihood = -1203.3269  
Iteration 2:   log profile likelihood = -1203.3269  

Fitting final Cox model:
Iteration 0:   log likelihood = -1538.7067
Iteration 1:   log likelihood = -1389.4444
Iteration 2:   log likelihood = -1249.7072
Iteration 3:   log likelihood = -1212.7975
Iteration 4:   log likelihood = -1204.6881
Iteration 5:   log likelihood = -1203.4328
Iteration 6:   log likelihood = -1203.3289
Iteration 7:   log likelihood = -1203.3269
Iteration 8:   log likelihood = -1203.3269
Refining estimates:
Iteration 0:   log likelihood = -1203.3269

Cox regression with Breslow method for ties
Gamma shared frailty                                Number of obs     =  4,510
Group variable: gwf_caseid                          Number of groups  =    279
                                                    Obs per group:   
No. of subjects =  4,510                                          min =      1
No. of failures =    182                                          avg =     16
Time at risk    = 75,603                                          max =     65
                                                    Wald chi2(6)      =  65.98
Log likelihood = -1203.3269                         Prob > chi2       = 0.0000

------------------------------------------------------------------------------
          _t | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
          lt |  -.8265787   .2763192    -2.99   0.003    -1.368154    -.285003
     coldwar |  -1.250344   .2786141    -4.49   0.000    -1.796418   -.7042703
    lnregion |   .2345289   .0773465     3.03   0.002     .0829325    .3861253
      xpers2 |  -.2431332   .1611091    -1.51   0.131    -.5589012    .0726348
       lpopl |    .357789   .1891552     1.89   0.059    -.0129483    .7285262
       stXlt |   .3633888   .1039346     3.50   0.000     .1596808    .5670969
-------------+----------------------------------------------------------------
       theta |   6.554958   1.005058
------------------------------------------------------------------------------
LR test of theta=0: chibar2(01) = 236.08               Prob >= chibar2 = 0.000

Note: Standard errors of regression parameters are conditional on theta.

.                         estat phtest,detail     

Test of proportional-hazards assumption

Time function: Analysis time
--------------------------------------------------------
             |        rho     chi2       df    Prob>chi2
-------------+------------------------------------------
          lt |    0.02265     0.21        1       0.6461
     coldwar |    0.01916     0.22        1       0.6402
    lnregion |    0.00506     0.01        1       0.9386
      xpers2 |   -0.00529     0.02        1       0.8901
       lpopl |    0.02360     1.29        1       0.2554
       stXlt |   -0.01350     0.07        1       0.7869
-------------+------------------------------------------
 Global test |                1.78        6       0.9385
--------------------------------------------------------

.                         centile xpers2,centile(10 90)

                                                          Binom. interp.   
    Variable |       Obs  Percentile    Centile        [95% conf. interval]
-------------+-------------------------------------------------------------
      xpers2 |     4,559         10   -1.551618       -1.551618   -1.551618
             |                   90    1.219388        1.219388    1.219388

.                         centile lnregion,centile(10 90)

                                                          Binom. interp.   
    Variable |       Obs  Percentile    Centile        [95% conf. interval]
-------------+-------------------------------------------------------------
    lnregion |     4,559         10   -.6586505       -.6586505   -.6586505
             |                   90     1.78987         1.78987     1.78987

.                         stcurve, hazard at1(xpers2=-1.6) at2(xpers2=1.3) title
> (Loyalty) ylab(0(.1).4)  ///
>                                 xtitle(Time since last protest onset,height(6)
> ) saving(r1.gph,replace) ///
>                                 legend(lab(1 "Low loyalty") lab(2 "High loyalt
> y") pos(5) col(1) ring(0)) range(1 40)  
note: all functions evaluated at frailty equal to one.
file r1.gph saved

.                         stcurve, hazard  at1(lnregion=-.7) at2(lnregion=1.8) t
> itle(Regional protest) ylab(0(.1).4) ///
>                                 xtitle(Time since last protest onset,height(6)
> ) saving(r2.gph,replace) ///
>                                 legend(lab(1 "Low regional protest") lab(2 "Hi
> gh regional protest") pos(5) col(1) ring(0)) range(1 40) 
note: all functions evaluated at frailty equal to one.
(file r2.gph not found)
file r2.gph saved

.                         gr combine r1.gph r2.gph,  

.                         graph export "$dir\cox-models.pdf", as(pdf)   replace
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\cox-models.pdf saved as PDF format

.                         
.                 *** IVSLS test ***
.                         use temp-fe,clear

.                         ** Leader effects **
.                         xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                         qui xtprobit xonset coldwar lxyrs lt lnregion xpers2, 
> re vce(cluster gwf_caseid)

.                         margins,dydx(xpers2)

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(xonset=1), predict(pr)
dy/dx wrt:  xpers2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0136002   .0044733    -3.04   0.002    -.0223677   -.0048326
------------------------------------------------------------------------------

.                         xtset gwf_leaderid year

Panel variable: gwf_leaderid (unbalanced)
 Time variable: year, 1946 to 2010, but with gaps
         Delta: 1 unit

.                         qui xtlogit xonset coldwar lxyrs lt lnregion xpers2, r
> e vce(cluster gwf_leaderid)

.                         margins,dydx(xpers2)

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(xonset=1), predict(pr)
dy/dx wrt:  xpers2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0158221   .0061861    -2.56   0.011    -.0279467   -.0036975
------------------------------------------------------------------------------

.                         qui xtlogit xonset coldwar lxyrs lt lnregion xpers2, f
> e  

.                         margins,dydx(xpers2)

Average marginal effects                                 Number of obs = 1,825
Model VCE: OIM

Expression: Pr(xonset|fixed effect is 0), predict(pu0)
dy/dx wrt:  xpers2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0840908   .0421168    -2.00   0.046    -.1666383   -.0015434
------------------------------------------------------------------------------

.                                 * Leader-specific time trend *
.                         xi:qui reg xonset i.gwf_leaderid*time lxyrs lt lnregio
> n xpers2,cluster(gwf_leaderid)
i.gwf_leaderid    _Igwf_leade_1-505   (naturally coded; _Igwf_leade_1 omitted)
i.gwf_le~d*time   _IgwfXtim_#         (coded as above)

.                         lincom xpers

 ( 1)  xpers2 = 0

------------------------------------------------------------------------------
      xonset | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0280951   .0138174    -2.03   0.043     -.055242   -.0009482
------------------------------------------------------------------------------

.                         
.                         ** IV **
.                         egen rcount = count(year),by(gwf_caseid)

.                         keep if rcount>=2
(24 observations deleted)

.                         xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                         gen milit2 = militrank^2

.                         xtreg xonset i.year lxyrs lt lnregion xpers2,re vce(cl
> uster gwf_caseid)

Random-effects GLS regression                   Number of obs     =      4,535
Group variable: gwf_caseid                      Number of groups  =        256

R-squared:                                      Obs per group:
     Within  = 0.0448                                         min =          2
     Between = 0.0552                                         avg =       17.7
     Overall = 0.0442                                         max =         65

                                                Wald chi2(68)     =     270.77
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                           (Std. err. adjusted for 256 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        year |
       1947  |   .0035157   .0026368     1.33   0.182    -.0016524    .0086837
       1948  |   .0057854    .004033     1.43   0.151    -.0021192    .0136901
       1949  |   .0151878   .0049856     3.05   0.002     .0054161    .0249595
       1950  |   .0144309   .0054034     2.67   0.008     .0038405    .0250213
       1951  |   .0372101   .0249606     1.49   0.136    -.0117118    .0861319
       1952  |   .0346722   .0244427     1.42   0.156    -.0132347     .082579
       1953  |   .0123703   .0072804     1.70   0.089     -.001899    .0266396
       1954  |   .0177522   .0074515     2.38   0.017     .0031475    .0323569
       1955  |    .018106   .0075637     2.39   0.017     .0032813    .0329307
       1956  |   .1012509   .0399219     2.54   0.011     .0230055    .1794963
       1957  |   .0949713   .0408719     2.32   0.020     .0148638    .1750788
       1958  |  -.0000557   .0086734    -0.01   0.995    -.0170552    .0169438
       1959  |   .0183142   .0080874     2.26   0.024     .0024633    .0341652
       1960  |    .058171   .0284237     2.05   0.041     .0024617    .1138804
       1961  |   .0242922   .0179868     1.35   0.177    -.0109611    .0595456
       1962  |   .0120649   .0075938     1.59   0.112    -.0028186    .0269484
       1963  |   .0458237   .0224957     2.04   0.042     .0017329    .0899145
       1964  |   .0496243   .0249918     1.99   0.047     .0006413    .0986073
       1965  |   .0100316   .0088768     1.13   0.258    -.0073666    .0274298
       1966  |   .0570538   .0236207     2.42   0.016      .010758    .1033497
       1967  |    .041809   .0235366     1.78   0.076    -.0043218    .0879399
       1968  |   .1224373   .0340909     3.59   0.000     .0556204    .1892542
       1969  |   .0300951   .0181445     1.66   0.097    -.0054675    .0656577
       1970  |   .0293455   .0141137     2.08   0.038     .0016832    .0570078
       1971  |    .029214   .0142054     2.06   0.040      .001372    .0570561
       1972  |   .0269206   .0144322     1.87   0.062     -.001366    .0552072
       1973  |   .0341427   .0179819     1.90   0.058    -.0011011    .0693866
       1974  |   .0238603   .0138465     1.72   0.085    -.0032783    .0509988
       1975  |   .0317431   .0154379     2.06   0.040     .0014853    .0620009
       1976  |   .0527997   .0203519     2.59   0.009     .0129107    .0926887
       1977  |   .0922284   .0278799     3.31   0.001     .0375848    .1468719
       1978  |   .0316253   .0180308     1.75   0.079    -.0037144    .0669649
       1979  |   .0616147   .0233561     2.64   0.008     .0158377    .1073917
       1980  |   .0302675   .0149651     2.02   0.043     .0009364    .0595987
       1981  |   .0421834   .0191747     2.20   0.028     .0046018     .079765
       1982  |   .0312145   .0140758     2.22   0.027     .0036264    .0588027
       1983  |   .0796481   .0257038     3.10   0.002     .0292696    .1300266
       1984  |   .0283609   .0182228     1.56   0.120     -.007355    .0640769
       1985  |   .0488125    .023005     2.12   0.034     .0037235    .0939016
       1986  |    .049249   .0232611     2.12   0.034     .0036582    .0948398
       1987  |   .1003967   .0320585     3.13   0.002     .0375632    .1632302
       1988  |   .0781752   .0304372     2.57   0.010     .0185195    .1378309
       1989  |   .1437177   .0365009     3.94   0.000     .0721772    .2152582
       1990  |    .186759   .0446806     4.18   0.000     .0991866    .2743314
       1991  |    .077863   .0379166     2.05   0.040     .0035479    .1521781
       1992  |   .0277431   .0200384     1.38   0.166    -.0115314    .0670176
       1993  |   .0408194   .0254428     1.60   0.109    -.0090475    .0906862
       1994  |   .0029703   .0111371     0.27   0.790    -.0188581    .0247986
       1995  |   .0348403   .0163414     2.13   0.033     .0028116    .0668689
       1996  |   .0676629    .029755     2.27   0.023     .0093442    .1259816
       1997  |   .0475325   .0225344     2.11   0.035     .0033658    .0916991
       1998  |    .054665   .0254741     2.15   0.032     .0047367    .1045933
       1999  |   .0764061    .029857     2.56   0.010     .0178875    .1349246
       2000  |   .0979011   .0372509     2.63   0.009     .0248905    .1709116
       2001  |   .0298138    .023563     1.27   0.206    -.0163688    .0759964
       2002  |   .0237925   .0217572     1.09   0.274    -.0188508    .0664359
       2003  |   .0751042   .0330942     2.27   0.023     .0102408    .1399675
       2004  |   .0614895   .0315568     1.95   0.051    -.0003607    .1233397
       2005  |   .0888134   .0350713     2.53   0.011     .0200749    .1575519
       2006  |   .0322756    .027202     1.19   0.235    -.0210393    .0855906
       2007  |   .1341024    .043795     3.06   0.002     .0482657    .2199391
       2008  |   .0473694   .0298807     1.59   0.113    -.0111957    .1059346
       2009  |    .040417   .0275681     1.47   0.143    -.0136155    .0944494
       2010  |   .0716251   .0371667     1.93   0.054    -.0012203    .1444704
             |
       lxyrs |  -.0070275    .004017    -1.75   0.080    -.0149007    .0008457
          lt |   .0074236   .0029064     2.55   0.011      .001727    .0131201
    lnregion |   .0148413   .0039311     3.78   0.000     .0071366     .022546
      xpers2 |  -.0105546   .0033845    -3.12   0.002    -.0171882    -.003921
       _cons |  -.0115571   .0093767    -1.23   0.218    -.0299351    .0068209
-------------+----------------------------------------------------------------
     sigma_u |  .01650544
     sigma_e |  .18631015
         rho |  .00778728   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                         est store iv1

.                         xtivreg xonset i.year lxyrs lt lnregion (xpers2=militr
> ank milit2), ///
>                                 re vce(cluster gwf_caseid)  ec2sls 

EC2SLS random-effects IV regression             Number of obs     =      4,535
Group variable: gwf_caseid                      Number of groups  =        256

R-squared:                                      Obs per group:
     Within  = 0.0543                                         min =          2
     Between = 0.0001                                         avg =       17.7
     Overall = 0.0343                                         max =         65


                                                Wald chi2(68)     =     230.34
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                           (Std. err. adjusted for 256 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0288798   .0080542    -3.59   0.000    -.0446657   -.0130938
             |
        year |
       1947  |  -.0046335   .0042216    -1.10   0.272    -.0129076    .0036407
       1948  |  -.0073828   .0055589    -1.33   0.184     -.018278    .0035125
       1949  |  -.0005463    .007347    -0.07   0.941    -.0149461    .0138535
       1950  |  -.0087487    .007863    -1.11   0.266    -.0241599    .0066625
       1951  |   .0102375   .0245032     0.42   0.676    -.0377879    .0582629
       1952  |   .0083388   .0263229     0.32   0.751    -.0432531    .0599306
       1953  |  -.0168412   .0105315    -1.60   0.110    -.0374825    .0038001
       1954  |   -.015919   .0104893    -1.52   0.129    -.0364776    .0046395
       1955  |  -.0172656   .0110823    -1.56   0.119    -.0389866    .0044554
       1956  |   .0627838    .039668     1.58   0.113    -.0149641    .1405318
       1957  |   .0631338   .0389409     1.62   0.105     -.013189    .1394567
       1958  |  -.0347574   .0123163    -2.82   0.005    -.0588968   -.0106179
       1959  |   -.019179    .011334    -1.69   0.091    -.0413932    .0030352
       1960  |   .0197262   .0277856     0.71   0.478    -.0347326    .0741849
       1961  |  -.0087606   .0203687    -0.43   0.667    -.0486826    .0311614
       1962  |  -.0240858    .011177    -2.15   0.031    -.0459923   -.0021792
       1963  |   .0079201   .0222362     0.36   0.722    -.0356621    .0515022
       1964  |   .0117478   .0254049     0.46   0.644    -.0380448    .0615404
       1965  |  -.0266987   .0133181    -2.00   0.045    -.0528017   -.0005957
       1966  |   .0201852   .0234891     0.86   0.390    -.0258526     .066223
       1967  |   .0091178   .0269991     0.34   0.736    -.0437994     .062035
       1968  |   .0896074   .0347112     2.58   0.010     .0215746    .1576401
       1969  |  -.0002237    .020093    -0.01   0.991    -.0396053    .0391579
       1970  |  -.0033842   .0168452    -0.20   0.841    -.0364001    .0296318
       1971  |  -.0048667   .0184338    -0.26   0.792    -.0409962    .0312629
       1972  |  -.0071881   .0174179    -0.41   0.680    -.0413266    .0269504
       1973  |  -.0012396   .0204444    -0.06   0.952    -.0413098    .0388307
       1974  |    -.01127   .0175799    -0.64   0.521    -.0457258    .0231859
       1975  |  -.0041444   .0203475    -0.20   0.839    -.0440248     .035736
       1976  |    .019105   .0233453     0.82   0.413    -.0266509     .064861
       1977  |   .0581791   .0291873     1.99   0.046     .0009731    .1153851
       1978  |   .0003276    .021894     0.01   0.988    -.0425839    .0432391
       1979  |   .0296357   .0254424     1.16   0.244    -.0202305    .0795018
       1980  |   .0002974   .0185911     0.02   0.987    -.0361405    .0367354
       1981  |   .0097004   .0226821     0.43   0.669    -.0347558    .0541565
       1982  |  -.0026201    .018336    -0.14   0.886    -.0385581    .0333179
       1983  |   .0449379    .026525     1.69   0.090    -.0070501     .096926
       1984  |  -.0053693    .021924    -0.24   0.807    -.0483396     .037601
       1985  |   .0145829   .0266276     0.55   0.584    -.0376062    .0667721
       1986  |   .0158784   .0262266     0.61   0.545    -.0355248    .0672817
       1987  |   .0677992   .0347118     1.95   0.051    -.0002347    .1358331
       1988  |   .0493214   .0344031     1.43   0.152    -.0181074    .1167503
       1989  |   .1143683   .0385986     2.96   0.003     .0387165    .1900201
       1990  |   .1632082   .0465488     3.51   0.000     .0719743    .2544422
       1991  |   .0638369   .0410967     1.55   0.120     -.016711    .1443849
       1992  |   .0119176   .0233679     0.51   0.610    -.0338826    .0577177
       1993  |   .0232562   .0286968     0.81   0.418    -.0329885    .0795009
       1994  |   -.015487    .016528    -0.94   0.349    -.0478813    .0169073
       1995  |   .0099116   .0199756     0.50   0.620    -.0292398     .049063
       1996  |   .0421312   .0286449     1.47   0.141    -.0140119    .0982742
       1997  |    .020948   .0264947     0.79   0.429    -.0309806    .0728766
       1998  |   .0288512   .0289275     1.00   0.319    -.0278457    .0855481
       1999  |   .0494812   .0306044     1.62   0.106    -.0105022    .1094647
       2000  |   .0703484   .0374009     1.88   0.060     -.002956    .1436527
       2001  |   .0066295    .027448     0.24   0.809    -.0471676    .0604267
       2002  |   .0000676   .0245286     0.00   0.998    -.0480075    .0481428
       2003  |   .0491825   .0339708     1.45   0.148     -.017399    .1157641
       2004  |   .0356036   .0315489     1.13   0.259    -.0262312    .0974384
       2005  |   .0643868   .0367137     1.75   0.079    -.0075708    .1363444
       2006  |   .0082589   .0284617     0.29   0.772     -.047525    .0640427
       2007  |   .1092621   .0457317     2.39   0.017     .0196296    .1988947
       2008  |   .0271984   .0317313     0.86   0.391    -.0349938    .0893906
       2009  |   .0179464   .0303696     0.59   0.555    -.0415769    .0774698
       2010  |   .0483438   .0375019     1.29   0.197    -.0251586    .1218461
             |
       lxyrs |   .0097837   .0050154     1.95   0.051    -.0000463    .0196137
          lt |   .0109117    .004007     2.72   0.006     .0030581    .0187652
    lnregion |   .0140048   .0039282     3.57   0.000     .0063057    .0217039
       _cons |   .0271597   .0145242     1.87   0.061    -.0013071    .0556265
-------------+----------------------------------------------------------------
     sigma_u |  .06898264
     sigma_e |  .18774061
         rho |  .11894984   (fraction of variance due to u_i)
------------------------------------------------------------------------------
Instrumented: xpers2
 Instruments: 1947.year 1948.year 1949.year 1950.year 1951.year 1952.year
              1953.year 1954.year 1955.year 1956.year 1957.year 1958.year
              1959.year 1960.year 1961.year 1962.year 1963.year 1964.year
              1965.year 1966.year 1967.year 1968.year 1969.year 1970.year
              1971.year 1972.year 1973.year 1974.year 1975.year 1976.year
              1977.year 1978.year 1979.year 1980.year 1981.year 1982.year
              1983.year 1984.year 1985.year 1986.year 1987.year 1988.year
              1989.year 1990.year 1991.year 1992.year 1993.year 1994.year
              1995.year 1996.year 1997.year 1998.year 1999.year 2000.year
              2001.year 2002.year 2003.year 2004.year 2005.year 2006.year
              2007.year 2008.year 2009.year 2010.year lxyrs lt lnregion
              militrank milit2

.                         est store iv2

.                                 qui: xtreg xpers2 i.year lxyrs lt lnregion   m
> ilitrank milit2,vce(cluster gwf_caseid)

.                                 test militrank=0

 ( 1)  militrank = 0

           chi2(  1) =    3.03
         Prob > chi2 =    0.0819

.                                 test milit2=0,ac

 ( 1)  militrank = 0
 ( 2)  milit2 = 0

           chi2(  2) =    7.76
         Prob > chi2 =    0.0206

.                         reghdfe xonset lxyrs lt lnregion xpers2,a(gwf_caseid y
> ear) cluster(gwf_leaderid) 
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =      4,535
Absorbing 2 HDFE groups                           F(   4,    480) =       6.76
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1406
                                                  Adj R-squared   =     0.0747
                                                  Within R-sq.    =     0.0105
Number of clusters (gwf_leaderid) =        481    Root MSE        =     0.1863

                         (Std. err. adjusted for 481 clusters in gwf_leaderid)
------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       lxyrs |   .0225382   .0064623     3.49   0.001     .0098404    .0352361
          lt |   .0062894     .00412     1.53   0.128     -.001806    .0143849
    lnregion |   .0130137   .0040993     3.17   0.002     .0049588    .0210685
      xpers2 |  -.0105159    .006309    -1.67   0.096    -.0229125    .0018807
       _cons |   .0388731   .0019865    19.57   0.000     .0349698    .0427763
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  gwf_caseid |       256           0         256     |
        year |        65           1          64     |
-----------------------------------------------------+

.                         est store iv3

.                         xi:ivreg2h xonset i.year lxyrs lt lnregion (xpers2=mil
> itrank milit2), ///
>                                 fe cluster(gwf_leaderid) gmm2s partial(i.year)
i.year            _Iyear_1946-2010    (naturally coded; _Iyear_1946 omitted)

Standard IV Results
Fixed Effects by(gwf_caseid), 256 groups

2-Step GMM estimation
---------------------

Estimates efficient for arbitrary heteroskedasticity and clustering on gwf_leade
> rid
Statistics robust to heteroskedasticity and clustering on gwf_leaderid

Number of clusters (gwf_leaderid) = 481               Number of obs =     4535
                                                      F(  4,   480) =     6.64
                                                      Prob > F      =   0.0000
Total (centered) SS     =  147.7228895                Centered R2   =  -0.0082
Total (uncentered) SS   =  147.7228895                Uncentered R2 =  -0.0082
Residual SS             =  148.9354404                Root MSE      =    .1866

------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0655615   .0349912    -1.87   0.061    -.1341429      .00302
       lxyrs |   .0252271   .0064274     3.92   0.000     .0126295    .0378246
          lt |   .0202623   .0087541     2.31   0.021     .0031046    .0374199
    lnregion |   .0130142   .0039867     3.26   0.001     .0052004     .020828
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):              8.447
                                                   Chi-sq(2) P-val =    0.0146
------------------------------------------------------------------------------
Weak identification test (Kleibergen-Paap rk Wald F statistic):          5.511
Stock-Yogo weak ID test critical values: 10% maximal IV size             19.93
                                         15% maximal IV size             11.59
                                         20% maximal IV size              8.75
                                         25% maximal IV size              7.25
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         2.579
                                                   Chi-sq(1) P-val =    0.1083
------------------------------------------------------------------------------
Instrumented:         xpers2
Included instruments: lxyrs lt lnregion
Excluded instruments: militrank milit2
Partialled-out:       _Iyear_1947 _Iyear_1948 _Iyear_1949 _Iyear_1950
                      _Iyear_1951 _Iyear_1952 _Iyear_1953 _Iyear_1954
                      _Iyear_1955 _Iyear_1956 _Iyear_1957 _Iyear_1958
                      _Iyear_1959 _Iyear_1960 _Iyear_1961 _Iyear_1962
                      _Iyear_1963 _Iyear_1964 _Iyear_1965 _Iyear_1966
                      _Iyear_1967 _Iyear_1968 _Iyear_1969 _Iyear_1970
                      _Iyear_1971 _Iyear_1972 _Iyear_1973 _Iyear_1974
                      _Iyear_1975 _Iyear_1976 _Iyear_1977 _Iyear_1978
                      _Iyear_1979 _Iyear_1980 _Iyear_1981 _Iyear_1982
                      _Iyear_1983 _Iyear_1984 _Iyear_1985 _Iyear_1986
                      _Iyear_1987 _Iyear_1988 _Iyear_1989 _Iyear_1990
                      _Iyear_1991 _Iyear_1992 _Iyear_1993 _Iyear_1994
                      _Iyear_1995 _Iyear_1996 _Iyear_1997 _Iyear_1998
                      _Iyear_1999 _Iyear_2000 _Iyear_2001 _Iyear_2002
                      _Iyear_2003 _Iyear_2004 _Iyear_2005 _Iyear_2006
                      _Iyear_2007 _Iyear_2008 _Iyear_2009 _Iyear_2010
                      nb: small-sample adjustments account for
                          partialled-out variables
------------------------------------------------------------------------------

Warning: variables have been centered

IV with Generated Instruments only
Fixed Effects by(gwf_caseid), 256 groups
Instruments created from Z:
_Iyear_1947 _Iyear_1948 _Iyear_1949 _Iyear_1950 _Iyear_1951 _Iyear_1952 _Iyear_1
> 953 _Iyear_1954 _Iyear_1955 _Iyear_1956 _Iyear_1957 _Iyear_1958 _Iyear_1959 _I
> year_1960 _Iyear_1961 _Iyear_1962 _Iyear_1963 _Iyear_1964 _Iyear_1965 _Iyear_1
> 966 _Iyear_1967 _Iyear_1968 _Iyear_1969 _Iyear_1970 _Iyear_1971 _Iyear_1972 _I
> year_1973 _Iyear_1974 _Iyear_1975 _Iyear_1976 _Iyear_1977 _Iyear_1978 _Iyear_1
> 979 _Iyear_1980 _Iyear_1981 _Iyear_1982 _Iyear_1983 _Iyear_1984 _Iyear_1985 _I
> year_1986 _Iyear_1987 _Iyear_1988 _Iyear_1989 _Iyear_1990 _Iyear_1991 _Iyear_1
> 992 _Iyear_1993 _Iyear_1994 _Iyear_1995 _Iyear_1996 _Iyear_1997 _Iyear_1998 _I
> year_1999 _Iyear_2000 _Iyear_2001 _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2
> 005 _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009 _Iyear_2010 lxyrs lt lnreg
> ion

2-Step GMM estimation
---------------------

Estimates efficient for arbitrary heteroskedasticity and clustering on gwf_leade
> rid
Statistics robust to heteroskedasticity and clustering on gwf_leaderid

Number of clusters (gwf_leaderid) = 481               Number of obs =     4535
                                                      F(  4,   480) =    13.87
                                                      Prob > F      =   0.0000
Total (centered) SS     =  147.7228895                Centered R2   =   0.0087
Total (uncentered) SS   =  147.7228895                Uncentered R2 =   0.0087
Residual SS             =  146.4440159                Root MSE      =     .185

------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0268812   .0065686    -4.09   0.000    -.0397554   -.0140071
       lxyrs |   .0215075   .0046135     4.66   0.000     .0124652    .0305499
          lt |   .0121359    .003262     3.72   0.000     .0057425    .0185294
    lnregion |   .0104198   .0030205     3.45   0.001     .0044997      .01634
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):             91.132
                                                   Chi-sq(67) P-val =   0.0266
------------------------------------------------------------------------------
Weak identification test (Kleibergen-Paap rk Wald F statistic):          8.399
Stock-Yogo weak ID test critical values:  5% maximal IV relative bias    21.22
                                         10% maximal IV relative bias    11.00
                                         20% maximal IV relative bias     5.77
                                         30% maximal IV relative bias     3.99
                                         10% maximal IV size            174.50
                                         15% maximal IV size             89.28
                                         20% maximal IV size             60.54
                                         25% maximal IV size             46.18
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):        57.422
                                                   Chi-sq(66) P-val =   0.7652
------------------------------------------------------------------------------
Instrumented:         xpers2
Included instruments: lxyrs lt lnregion
Excluded instruments: xpers2__Iyear_1947_g xpers2__Iyear_1948_g
                      xpers2__Iyear_1949_g xpers2__Iyear_1950_g
                      xpers2__Iyear_1951_g xpers2__Iyear_1952_g
                      xpers2__Iyear_1953_g xpers2__Iyear_1954_g
                      xpers2__Iyear_1955_g xpers2__Iyear_1956_g
                      xpers2__Iyear_1957_g xpers2__Iyear_1958_g
                      xpers2__Iyear_1959_g xpers2__Iyear_1960_g
                      xpers2__Iyear_1961_g xpers2__Iyear_1962_g
                      xpers2__Iyear_1963_g xpers2__Iyear_1964_g
                      xpers2__Iyear_1965_g xpers2__Iyear_1966_g
                      xpers2__Iyear_1967_g xpers2__Iyear_1968_g
                      xpers2__Iyear_1969_g xpers2__Iyear_1970_g
                      xpers2__Iyear_1971_g xpers2__Iyear_1972_g
                      xpers2__Iyear_1973_g xpers2__Iyear_1974_g
                      xpers2__Iyear_1975_g xpers2__Iyear_1976_g
                      xpers2__Iyear_1977_g xpers2__Iyear_1978_g
                      xpers2__Iyear_1979_g xpers2__Iyear_1980_g
                      xpers2__Iyear_1981_g xpers2__Iyear_1982_g
                      xpers2__Iyear_1983_g xpers2__Iyear_1984_g
                      xpers2__Iyear_1985_g xpers2__Iyear_1986_g
                      xpers2__Iyear_1987_g xpers2__Iyear_1988_g
                      xpers2__Iyear_1989_g xpers2__Iyear_1990_g
                      xpers2__Iyear_1991_g xpers2__Iyear_1992_g
                      xpers2__Iyear_1993_g xpers2__Iyear_1994_g
                      xpers2__Iyear_1995_g xpers2__Iyear_1996_g
                      xpers2__Iyear_1997_g xpers2__Iyear_1998_g
                      xpers2__Iyear_1999_g xpers2__Iyear_2000_g
                      xpers2__Iyear_2001_g xpers2__Iyear_2002_g
                      xpers2__Iyear_2003_g xpers2__Iyear_2004_g
                      xpers2__Iyear_2005_g xpers2__Iyear_2006_g
                      xpers2__Iyear_2007_g xpers2__Iyear_2008_g
                      xpers2__Iyear_2009_g xpers2__Iyear_2010_g xpers2_lxyrs_g
                      xpers2_lt_g xpers2_lnregion_g
Partialled-out:       _Iyear_1947 _Iyear_1948 _Iyear_1949 _Iyear_1950
                      _Iyear_1951 _Iyear_1952 _Iyear_1953 _Iyear_1954
                      _Iyear_1955 _Iyear_1956 _Iyear_1957 _Iyear_1958
                      _Iyear_1959 _Iyear_1960 _Iyear_1961 _Iyear_1962
                      _Iyear_1963 _Iyear_1964 _Iyear_1965 _Iyear_1966
                      _Iyear_1967 _Iyear_1968 _Iyear_1969 _Iyear_1970
                      _Iyear_1971 _Iyear_1972 _Iyear_1973 _Iyear_1974
                      _Iyear_1975 _Iyear_1976 _Iyear_1977 _Iyear_1978
                      _Iyear_1979 _Iyear_1980 _Iyear_1981 _Iyear_1982
                      _Iyear_1983 _Iyear_1984 _Iyear_1985 _Iyear_1986
                      _Iyear_1987 _Iyear_1988 _Iyear_1989 _Iyear_1990
                      _Iyear_1991 _Iyear_1992 _Iyear_1993 _Iyear_1994
                      _Iyear_1995 _Iyear_1996 _Iyear_1997 _Iyear_1998
                      _Iyear_1999 _Iyear_2000 _Iyear_2001 _Iyear_2002
                      _Iyear_2003 _Iyear_2004 _Iyear_2005 _Iyear_2006
                      _Iyear_2007 _Iyear_2008 _Iyear_2009 _Iyear_2010
                      nb: small-sample adjustments account for
                          partialled-out variables
------------------------------------------------------------------------------

Warning: variables have been centered

IV with Generated Instruments and External Instruments
Fixed Effects by(gwf_caseid), 256 groups
Testing Orthogonality of Instruments created from Z:
_Iyear_1947 _Iyear_1948 _Iyear_1949 _Iyear_1950 _Iyear_1951 _Iyear_1952 _Iyear_1
> 953 _Iyear_1954 _Iyear_1955 _Iyear_1956 _Iyear_1957 _Iyear_1958 _Iyear_1959 _I
> year_1960 _Iyear_1961 _Iyear_1962 _Iyear_1963 _Iyear_1964 _Iyear_1965 _Iyear_1
> 966 _Iyear_1967 _Iyear_1968 _Iyear_1969 _Iyear_1970 _Iyear_1971 _Iyear_1972 _I
> year_1973 _Iyear_1974 _Iyear_1975 _Iyear_1976 _Iyear_1977 _Iyear_1978 _Iyear_1
> 979 _Iyear_1980 _Iyear_1981 _Iyear_1982 _Iyear_1983 _Iyear_1984 _Iyear_1985 _I
> year_1986 _Iyear_1987 _Iyear_1988 _Iyear_1989 _Iyear_1990 _Iyear_1991 _Iyear_1
> 992 _Iyear_1993 _Iyear_1994 _Iyear_1995 _Iyear_1996 _Iyear_1997 _Iyear_1998 _I
> year_1999 _Iyear_2000 _Iyear_2001 _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2
> 005 _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009 _Iyear_2010 lxyrs lt lnreg
> ion

2-Step GMM estimation
---------------------

Estimates efficient for arbitrary heteroskedasticity and clustering on gwf_leade
> rid
Statistics robust to heteroskedasticity and clustering on gwf_leaderid

Number of clusters (gwf_leaderid) = 481               Number of obs =     4535
                                                      F(  4,   480) =    13.35
                                                      Prob > F      =   0.0000
Total (centered) SS     =  147.7228895                Centered R2   =   0.0088
Total (uncentered) SS   =  147.7228895                Uncentered R2 =   0.0088
Residual SS             =  146.4168793                Root MSE      =     .185

------------------------------------------------------------------------------
             |               Robust
      xonset | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0256544   .0064691    -3.97   0.000    -.0383336   -.0129751
       lxyrs |   .0211573   .0045691     4.63   0.000      .012202    .0301126
          lt |   .0117395   .0032497     3.61   0.000     .0053703    .0181087
    lnregion |   .0099267   .0030014     3.31   0.001      .004044    .0158094
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):             98.804
                                                   Chi-sq(69) P-val =   0.0108
------------------------------------------------------------------------------
Weak identification test (Kleibergen-Paap rk Wald F statistic):         10.995
Stock-Yogo weak ID test critical values:  5% maximal IV relative bias    21.21
                                         10% maximal IV relative bias    10.99
                                         20% maximal IV relative bias     5.76
                                         30% maximal IV relative bias     3.99
                                         10% maximal IV size            179.27
                                         15% maximal IV size             91.68
                                         20% maximal IV size             62.16
                                         25% maximal IV size             47.39
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):        60.143
                                                   Chi-sq(68) P-val =   0.7401
-orthog- option:
Hansen J statistic (eqn. excluding suspect orthog. conditions):         57.489
                                                   Chi-sq(66) P-val =   0.7632
C statistic (exogeneity/orthogonality of suspect instruments):           2.654
                                                   Chi-sq(2) P-val =    0.2652
Instruments tested:   militrank milit2
------------------------------------------------------------------------------
Instrumented:         xpers2
Included instruments: lxyrs lt lnregion
Excluded instruments: militrank milit2 xpers2__Iyear_1947_g xpers2__Iyear_1948_g
                      xpers2__Iyear_1949_g xpers2__Iyear_1950_g
                      xpers2__Iyear_1951_g xpers2__Iyear_1952_g
                      xpers2__Iyear_1953_g xpers2__Iyear_1954_g
                      xpers2__Iyear_1955_g xpers2__Iyear_1956_g
                      xpers2__Iyear_1957_g xpers2__Iyear_1958_g
                      xpers2__Iyear_1959_g xpers2__Iyear_1960_g
                      xpers2__Iyear_1961_g xpers2__Iyear_1962_g
                      xpers2__Iyear_1963_g xpers2__Iyear_1964_g
                      xpers2__Iyear_1965_g xpers2__Iyear_1966_g
                      xpers2__Iyear_1967_g xpers2__Iyear_1968_g
                      xpers2__Iyear_1969_g xpers2__Iyear_1970_g
                      xpers2__Iyear_1971_g xpers2__Iyear_1972_g
                      xpers2__Iyear_1973_g xpers2__Iyear_1974_g
                      xpers2__Iyear_1975_g xpers2__Iyear_1976_g
                      xpers2__Iyear_1977_g xpers2__Iyear_1978_g
                      xpers2__Iyear_1979_g xpers2__Iyear_1980_g
                      xpers2__Iyear_1981_g xpers2__Iyear_1982_g
                      xpers2__Iyear_1983_g xpers2__Iyear_1984_g
                      xpers2__Iyear_1985_g xpers2__Iyear_1986_g
                      xpers2__Iyear_1987_g xpers2__Iyear_1988_g
                      xpers2__Iyear_1989_g xpers2__Iyear_1990_g
                      xpers2__Iyear_1991_g xpers2__Iyear_1992_g
                      xpers2__Iyear_1993_g xpers2__Iyear_1994_g
                      xpers2__Iyear_1995_g xpers2__Iyear_1996_g
                      xpers2__Iyear_1997_g xpers2__Iyear_1998_g
                      xpers2__Iyear_1999_g xpers2__Iyear_2000_g
                      xpers2__Iyear_2001_g xpers2__Iyear_2002_g
                      xpers2__Iyear_2003_g xpers2__Iyear_2004_g
                      xpers2__Iyear_2005_g xpers2__Iyear_2006_g
                      xpers2__Iyear_2007_g xpers2__Iyear_2008_g
                      xpers2__Iyear_2009_g xpers2__Iyear_2010_g xpers2_lxyrs_g
                      xpers2_lt_g xpers2_lnregion_g
Partialled-out:       _Iyear_1947 _Iyear_1948 _Iyear_1949 _Iyear_1950
                      _Iyear_1951 _Iyear_1952 _Iyear_1953 _Iyear_1954
                      _Iyear_1955 _Iyear_1956 _Iyear_1957 _Iyear_1958
                      _Iyear_1959 _Iyear_1960 _Iyear_1961 _Iyear_1962
                      _Iyear_1963 _Iyear_1964 _Iyear_1965 _Iyear_1966
                      _Iyear_1967 _Iyear_1968 _Iyear_1969 _Iyear_1970
                      _Iyear_1971 _Iyear_1972 _Iyear_1973 _Iyear_1974
                      _Iyear_1975 _Iyear_1976 _Iyear_1977 _Iyear_1978
                      _Iyear_1979 _Iyear_1980 _Iyear_1981 _Iyear_1982
                      _Iyear_1983 _Iyear_1984 _Iyear_1985 _Iyear_1986
                      _Iyear_1987 _Iyear_1988 _Iyear_1989 _Iyear_1990
                      _Iyear_1991 _Iyear_1992 _Iyear_1993 _Iyear_1994
                      _Iyear_1995 _Iyear_1996 _Iyear_1997 _Iyear_1998
                      _Iyear_1999 _Iyear_2000 _Iyear_2001 _Iyear_2002
                      _Iyear_2003 _Iyear_2004 _Iyear_2005 _Iyear_2006
                      _Iyear_2007 _Iyear_2008 _Iyear_2009 _Iyear_2010
                      nb: small-sample adjustments account for
                          partialled-out variables
------------------------------------------------------------------------------

Warning: variables have been centered

-----------------------------------------------------------
    Variable |    StdIV         GenInst       GenExtInst   
-------------+---------------------------------------------
      xpers2 |      -.06556        -.02688        -.02565  
             |         .035         .00657         .00647  
       lxyrs |       .02523         .02151         .02116  
             |       .00643         .00461         .00457  
          lt |       .02026         .01214         .01174  
             |       .00875         .00326         .00325  
    lnregion |       .01301         .01042        .009927  
             |       .00399         .00302           .003  
-------------+---------------------------------------------
           N |         4535           4535           4535  
        rmse |         .187           .185           .185  
           j |         2.58           57.4           60.1  
         jdf |            1             66             68  
          jp |         .108           .765            .74  
-----------------------------------------------------------
                                               Legend: b/se

.                         est store iv5

.                         
.                          * Within IV-probit *
.                         ivprobit xonset $cvar (xpers2=militrank milit2) m_cold
> war m_lxyrs  m_lt m_xpers2 m_lnregion,vce(cluster gwf_leaderid)

Fitting exogenous probit model

Iteration 0:   log likelihood = -3107.1261  
Iteration 1:   log likelihood =  -2638.979  
Iteration 2:   log likelihood = -2637.7281  
Iteration 3:   log likelihood = -2637.7278  

Fitting full model

Iteration 0:   log pseudolikelihood = -7005.2203  (not concave)
Iteration 1:   log pseudolikelihood = -3557.7559  
Iteration 2:   log pseudolikelihood = -3454.3864  (not concave)
Iteration 3:   log pseudolikelihood = -3447.8892  (not concave)
Iteration 4:   log pseudolikelihood = -3446.1859  
Iteration 5:   log pseudolikelihood =  -3445.021  
Iteration 6:   log pseudolikelihood = -3444.9538  
Iteration 7:   log pseudolikelihood = -3444.9512  
Iteration 8:   log pseudolikelihood = -3444.9512  

Probit model with endogenous regressors                 Number of obs =  4,535
                                                        Wald chi2(10) = 180.63
Log pseudolikelihood = -3444.9512                       Prob > chi2   = 0.0000

                           (Std. err. adjusted for 481 clusters in gwf_leaderid)
--------------------------------------------------------------------------------
               |               Robust
               | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
---------------+----------------------------------------------------------------
        xpers2 |  -.8263226   .5425064    -1.52   0.128    -1.889616    .2369705
       coldwar |  -.5309626   .1800009    -2.95   0.003     -.883758   -.1781673
         lxyrs |   .3645039   .0941115     3.87   0.000     .1800488     .548959
            lt |   .3406981   .1316239     2.59   0.010     .0827199    .5986763
      lnregion |   .1513338   .0378618     4.00   0.000      .077126    .2255415
     m_coldwar |   .6088661   .2553331     2.38   0.017     .1084225     1.10931
       m_lxyrs |  -.5569873   .0799231    -6.97   0.000    -.7136337   -.4003409
          m_lt |  -.0555955   .0730343    -0.76   0.447    -.1987401    .0875492
      m_xpers2 |   .0197766   .0530451     0.37   0.709    -.0841898    .1237431
    m_lnregion |   .1300456   .1293137     1.01   0.315    -.1234047    .3834958
         _cons |  -2.026121   .1927255   -10.51   0.000    -2.403856   -1.648386
---------------+----------------------------------------------------------------
 corr(e.xpers2,|
      e.xonset)|   .2865083   .2489878                     -.2325564    .6785419
   sd(e.xpers2)|   .4498542    .019895                       .412503    .4905876
--------------------------------------------------------------------------------
Wald test of exogeneity (corr = 0): chi2(1) = 1.18        Prob > chi2 = 0.2772
Instrumented: xpers2
 Instruments: coldwar lxyrs lt lnregion m_coldwar m_lxyrs m_lt m_xpers2
              m_lnregion militrank milit2

.                         margins,dydx(xpers2)predict(pr)

Average marginal effects                                 Number of obs = 4,535
Model VCE: Robust

Expression: Average structural function probabilities, predict(pr)
dy/dx wrt:  xpers2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0631751   .0461717    -1.37   0.171      -.15367    .0273199
------------------------------------------------------------------------------

.                         
.                          ** IV tests table **
.                         estout iv1 iv2 iv3 iv5 using TableB3.tex, cells(b(star
>   fmt(%9.4f)) se(par fmt(%9.4f))) ///
>                                 stats(N N_clust widstat) style(tex) replace la
> bel starlevels(* 0.05) title(\label{tabB3})
(file TableB3.tex not found)
(output written to TableB3.tex)

.                 
.                 *******************************
.                 *** RMSE model fit analysis ***
.                 *******************************
.                         use temp-fe,clear

.                         xtset cowcode year

Panel variable: cowcode (unbalanced)
 Time variable: year, 1946 to 2010, but with gaps
         Delta: 1 unit

.                         gen l1poldurable = l.poldurable
(526 missing values generated)

.                         gen l1v2x_libdem=l.v2x_libdem
(180 missing values generated)

.                         gen l1ythbul = l.ythbul
(616 missing values generated)

.                         gen l1v2cltort=l1.v2cltort
(177 missing values generated)

.                         gen l1v2clkill=l1.v2clkill
(177 missing values generated)

.                         recode debruin_affcount (5 4 3=3)
(252 changes made to debruin_affcount)

.                         global seed = 2892742

.                         global k=10 /* folds Chenoweth and Ufelder use 5 folds
> */

.                         global y="xonset"

.                         global varA= "lag_xongoing l1v2cademmob loggdp logoil 
> support lt leadermil coup12 election ythbul4 wdipopurbmi wditrade"

.                         global varB= "polparcomp polpolcomp polexconst l1v2x_p
> olyarchy l1v2x_libdem l1v2x_partipdem l1v2x_freexp_altinf l1v2x_frassoc_thick 
> "

.                         global varC= "l1v2x_clpol l1v2x_jucon l1v2juhcind e_v2
> x_neopat lnregion legcomp excluded monoethnic  multiethnic civwar grow"

.                         global varD= "l1v2cltort l1v2clkill lag_repress l1pold
> urable milethnic_homo lag_milpersonnel lag_milspend effectivenumber debruin_cb
> count debruin_ha_cbcount lpop"

.                         local var = "$varA $varB $varC $varD"

.                         sum `var'

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
lag_xongoing |      4,386    .0631555    .2432702          0          1
l1v2cademmob |      3,189    -.669338     1.37753     -2.866      3.411
      loggdp |      4,485    .7161757    1.135679  -3.464003   5.006266
      logoil |      4,510    .3891641    .8894467          0    5.60555
supportparty |      4,559     .735249    .4412489          0          1
-------------+---------------------------------------------------------
          lt |      4,559   -3.97e-09           1  -1.830678   2.133352
   leadermil |      4,559    .3481027     .476421          0          1
      coup12 |      4,430    .0650113    .2465736          0          1
    election |      4,559    .0699715    .2551269          0          1
     ythbul4 |      4,108    .1882162    .0219646   .0821929   .2641603
-------------+---------------------------------------------------------
 wdipopurbmi |      3,965    39.47613    22.19333  -10.94158        100
    wditrade |      3,086    67.04954    48.03929   .3088029   439.6567
  polparcomp |      4,208   -1.720057    16.77146        -88          5
  polpolcomp |      4,208   -.8500475    17.10212        -88         10
  polexconst |      4,208   -1.108127    16.92511        -88          7
-------------+---------------------------------------------------------
l1v2x_poly~y |      4,553    .1989953    .1233899   .0089592   .7512801
l1v2x_libdem |      4,379    .1176834    .0964318   .0070282   .6469669
l1v2x_part~m |      4,553    .0983955    .0753445    .008401    .510688
l1v2x_free~f |      4,556    .2819727    .2201424   .0124248   .8746925
l1v2x_fras~k |      4,556    .2569685    .2363743   .0196438   .9021719
-------------+---------------------------------------------------------
 l1v2x_clpol |      4,556    .2833766    .2240771       .011       .929
 l1v2x_jucon |      4,556    .3273185     .223879       .005       .912
 l1v2juhcind |      4,556   -.8645261    1.085845     -3.066      2.492
e_v2x_neopat |      4,558    .2873363     .186101   .0136296   .8725979
    lnregion |      4,559    5.15e-09           1  -.6586505   3.975882
-------------+---------------------------------------------------------
  legcompetn |      4,559    4.189735    2.963463          0          8
    excluded |      4,476    2.108607    1.652255          0    4.59512
  monoethnic |      4,559    .2268041    .4188108          0          1
 multiethnic |      4,559    .5064707     .500013          0          1
      civwar |      4,559    .0304891    .1719478          0          1
-------------+---------------------------------------------------------
        grow |      4,399    .0223924    .0684832  -.5308724   .6573918
  l1v2cltort |      4,382   -.8578309    1.105775     -3.212      2.597
  l1v2clkill |      4,382   -.5553012    1.316988     -3.228      2.623
 lag_repress |      4,430    .4595625    .9393459  -2.616968   3.115439
l1poldurable |      4,033    16.47533    17.03592          0        110
-------------+---------------------------------------------------------
milethnic~mo |      4,559    .1629743    .3693828          0          1
lag_milper~l |      4,512    3.787282     1.70751          0   9.433564
lag_milspend |      4,296    11.84688    2.664263          0   19.57725
effectiven~r |      2,944    1.731193    .6267506          1   4.577271
debruin_cb~t |      2,795    1.439714    1.137094          0          4
-------------+---------------------------------------------------------
debruin_ha~t |      2,795    .7806798    1.012901          0          9
       lpopl |      4,510    9.109913    1.370855   5.605392    14.0988

.                         qui reg $y xyrs*

.                         keep if e(sample)==1  
(0 observations deleted)

.                         sutex year $y  $varA $varB $varC $varD xpers2, minmax 
>           
%------- Begin LaTeX code -------%

\begin{table}[htbp]\centering \caption{Summary statistics \label{sumstat}}
\begin{tabular}{l c c c c c}\hline\hline
\multicolumn{1}{c}{\textbf{Variable}} & \textbf{Mean}
 & \textbf{Std. Dev.}& \textbf{Min.} &  \textbf{Max.} & \textbf{N}\\ \hline
year & 1979.757 & 16.521 & 1946 & 2010 & 4559\\
xonset & 0.04 & 0.196 & 0 & 1 & 4559\\
lag\_xongoing & 0.063 & 0.243 & 0 & 1 & 4386\\
l1v2cademmob & -0.669 & 1.378 & -2.866 & 3.411 & 3189\\
loggdp & 0.716 & 1.136 & -3.464 & 5.006 & 4485\\
logoil & 0.389 & 0.889 & 0 & 5.606 & 4510\\
supportparty & 0.735 & 0.441 & 0 & 1 & 4559\\
lt & 0 & 1 & -1.831 & 2.133 & 4559\\
leadermil & 0.348 & 0.476 & 0 & 1 & 4559\\
coup12 & 0.065 & 0.247 & 0 & 1 & 4430\\
election & 0.07 & 0.255 & 0 & 1 & 4559\\
ythbul4 & 0.188 & 0.022 & 0.082 & 0.264 & 4108\\
wdipopurbmi & 39.476 & 22.193 & -10.942 & 100 & 3965\\
wditrade & 67.05 & 48.039 & 0.309 & 439.657 & 3086\\
polparcomp & -1.72 & 16.771 & -88 & 5 & 4208\\
polpolcomp & -0.85 & 17.102 & -88 & 10 & 4208\\
polexconst & -1.108 & 16.925 & -88 & 7 & 4208\\
l1v2x\_polyarchy & 0.199 & 0.123 & 0.009 & 0.751 & 4553\\
l1v2x\_libdem & 0.118 & 0.096 & 0.007 & 0.647 & 4379\\
l1v2x\_partipdem & 0.098 & 0.075 & 0.008 & 0.511 & 4553\\
l1v2x\_freexp\_altinf & 0.282 & 0.22 & 0.012 & 0.875 & 4556\\
l1v2x\_frassoc\_thick & 0.257 & 0.236 & 0.02 & 0.902 & 4556\\
l1v2x\_clpol & 0.283 & 0.224 & 0.011 & 0.929 & 4556\\
l1v2x\_jucon & 0.327 & 0.224 & 0.005 & 0.912 & 4556\\
l1v2juhcind & -0.865 & 1.086 & -3.066 & 2.492 & 4556\\
e\_v2x\_neopat & 0.287 & 0.186 & 0.014 & 0.873 & 4558\\
lnregion & 0 & 1 & -0.659 & 3.976 & 4559\\
legcompetn & 4.19 & 2.963 & 0 & 8 & 4559\\
excluded & 2.109 & 1.652 & 0 & 4.595 & 4476\\
monoethnic & 0.227 & 0.419 & 0 & 1 & 4559\\
multiethnic & 0.506 & 0.5 & 0 & 1 & 4559\\
civwar & 0.03 & 0.172 & 0 & 1 & 4559\\
grow & 0.022 & 0.068 & -0.531 & 0.657 & 4399\\
l1v2cltort & -0.858 & 1.106 & -3.212 & 2.597 & 4382\\
l1v2clkill & -0.555 & 1.317 & -3.228 & 2.623 & 4382\\
lag\_repress & 0.46 & 0.939 & -2.617 & 3.115 & 4430\\
l1poldurable & 16.475 & 17.036 & 0 & 110 & 4033\\
milethnic\_homo & 0.163 & 0.369 & 0 & 1 & 4559\\
lag\_milpersonnel & 3.787 & 1.708 & 0 & 9.434 & 4512\\
lag\_milspend & 11.847 & 2.664 & 0 & 19.577 & 4296\\
effectivenumber & 1.731 & 0.627 & 1 & 4.577 & 2944\\
debruin\_cbcount & 1.44 & 1.137 & 0 & 4 & 2795\\
debruin\_ha\_cbcount & 0.781 & 1.013 & 0 & 9 & 2795\\
lpopl & 9.109 & 1.371 & 5.605 & 14.099 & 4510\\
xpers2 & 0 & 1 & -1.552 & 1.75 & 4559\\
\hline
\end{tabular}
\end{table}
%------- End LaTeX code -------%

.                                 xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                         capture program drop crossvalrmse

.                         program define crossvalrmse
  1.                                 mat A=r(est)
  2.                                 mat U = J(rowsof(A),1,1)
  3.                                 mat c = U'*A
  4.                                 mat cm = c/rowsof(A)
  5.                                 mat list cm
  6.                         end

.                         
.                         gen base=.
(4,559 missing values generated)

.                         gen rmse=.
(4,559 missing values generated)

.                         gen n=_n

.                         gen varname = ""
(4,559 missing values generated)

.                         local var = " $varA $varB $varC $varD xpers2"   

.                         local i =1

.                         foreach v of local var {
  2.                                 di "`v'"
  3.                                 qui set seed $seed
  4.                                 qui crossfold reghdfe $y xyrs* if `v'~=.,a(
> gwf_caseid coldwar)  k($k) 
  5.                                 crossvalrmse
  6.                                 local r = cm[1,1]
  7.                                 qui replace base = `r'   if n==`i'
  8.                                 qui set seed $seed
  9.                                 qui crossfold reghdfe $y xyrs* `v',a(gwf_ca
> seid coldwar)  k($k) 
 10.                                 crossvalrmse
 11.                                 local r = cm[1,1]
 12.                                 qui replace rmse = `r'   if n==`i'
 13.                                 qui replace varname = "`v'" if n==`i'
 14.                                 local i = `i' +1
 15.                         }
lag_xongoing

symmetric cm[1,1]
         RMSE
c1  .19713116

symmetric cm[1,1]
         RMSE
c1  .19766411
l1v2cademmob

symmetric cm[1,1]
         RMSE
c1  .19697193

symmetric cm[1,1]
         RMSE
c1  .20354137
loggdp

symmetric cm[1,1]
         RMSE
c1  .19718349

symmetric cm[1,1]
         RMSE
c1  .19757464
logoil

symmetric cm[1,1]
         RMSE
c1  .19710579

symmetric cm[1,1]
         RMSE
c1  .19825835
support

symmetric cm[1,1]
         RMSE
c1  .19695354

symmetric cm[1,1]
         RMSE
c1  .19730393
lt

symmetric cm[1,1]
         RMSE
c1  .19695354

symmetric cm[1,1]
        RMSE
c1  .1969045
leadermil

symmetric cm[1,1]
         RMSE
c1  .19695354

symmetric cm[1,1]
         RMSE
c1  .19829189
coup12

symmetric cm[1,1]
         RMSE
c1  .19708807

symmetric cm[1,1]
        RMSE
c1  .1993268
election

symmetric cm[1,1]
         RMSE
c1  .19695354

symmetric cm[1,1]
         RMSE
c1  .19631453
ythbul4

symmetric cm[1,1]
         RMSE
c1  .19750591

symmetric cm[1,1]
        RMSE
c1  .2057807
wdipopurbmi

symmetric cm[1,1]
         RMSE
c1  .19748128

symmetric cm[1,1]
         RMSE
c1  .20482516
wditrade

symmetric cm[1,1]
         RMSE
c1  .20024126

symmetric cm[1,1]
         RMSE
c1  .21170086
polparcomp

symmetric cm[1,1]
        RMSE
c1  .1972396

symmetric cm[1,1]
         RMSE
c1  .20303888
polpolcomp

symmetric cm[1,1]
        RMSE
c1  .1972396

symmetric cm[1,1]
         RMSE
c1  .20313796
polexconst

symmetric cm[1,1]
        RMSE
c1  .1972396

symmetric cm[1,1]
         RMSE
c1  .20312239
l1v2x_polyarchy

symmetric cm[1,1]
         RMSE
c1  .19697236

symmetric cm[1,1]
         RMSE
c1  .19695697
l1v2x_libdem

symmetric cm[1,1]
        RMSE
c1  .1971371

symmetric cm[1,1]
         RMSE
c1  .19770793
l1v2x_partipdem

symmetric cm[1,1]
         RMSE
c1  .19697236

symmetric cm[1,1]
         RMSE
c1  .19718493
l1v2x_freexp_altinf

symmetric cm[1,1]
        RMSE
c1  .1969553

symmetric cm[1,1]
         RMSE
c1  .19676573
l1v2x_frassoc_thick

symmetric cm[1,1]
        RMSE
c1  .1969553

symmetric cm[1,1]
         RMSE
c1  .19704407
l1v2x_clpol

symmetric cm[1,1]
        RMSE
c1  .1969553

symmetric cm[1,1]
         RMSE
c1  .19677854
l1v2x_jucon

symmetric cm[1,1]
        RMSE
c1  .1969553

symmetric cm[1,1]
         RMSE
c1  .19734228
l1v2juhcind

symmetric cm[1,1]
        RMSE
c1  .1969553

symmetric cm[1,1]
         RMSE
c1  .19733662
e_v2x_neopat

symmetric cm[1,1]
         RMSE
c1  .19695282

symmetric cm[1,1]
         RMSE
c1  .19824634
lnregion

symmetric cm[1,1]
         RMSE
c1  .19695354

symmetric cm[1,1]
         RMSE
c1  .19643983
legcomp

symmetric cm[1,1]
         RMSE
c1  .19695354

symmetric cm[1,1]
         RMSE
c1  .19679008
excluded

symmetric cm[1,1]
         RMSE
c1  .19714398

symmetric cm[1,1]
         RMSE
c1  .19852961
monoethnic

symmetric cm[1,1]
         RMSE
c1  .19695354

symmetric cm[1,1]
         RMSE
c1  .19695307
multiethnic

symmetric cm[1,1]
         RMSE
c1  .19695354

symmetric cm[1,1]
         RMSE
c1  .19703725
civwar

symmetric cm[1,1]
         RMSE
c1  .19695354

symmetric cm[1,1]
         RMSE
c1  .19705425
grow

symmetric cm[1,1]
         RMSE
c1  .19736687

symmetric cm[1,1]
         RMSE
c1  .19946296
l1v2cltort

symmetric cm[1,1]
         RMSE
c1  .19712719

symmetric cm[1,1]
        RMSE
c1  .1979763
l1v2clkill

symmetric cm[1,1]
         RMSE
c1  .19712719

symmetric cm[1,1]
         RMSE
c1  .19760498
lag_repress

symmetric cm[1,1]
         RMSE
c1  .19708807

symmetric cm[1,1]
         RMSE
c1  .19953919
l1poldurable

symmetric cm[1,1]
         RMSE
c1  .19744783

symmetric cm[1,1]
         RMSE
c1  .20490025
milethnic_homo

symmetric cm[1,1]
         RMSE
c1  .19695354

symmetric cm[1,1]
         RMSE
c1  .19743494
lag_milpersonnel

symmetric cm[1,1]
         RMSE
c1  .19695943

symmetric cm[1,1]
         RMSE
c1  .19880973
lag_milspend

symmetric cm[1,1]
         RMSE
c1  .19766902

symmetric cm[1,1]
         RMSE
c1  .20111597
effectivenumber

symmetric cm[1,1]
         RMSE
c1  .19849051

symmetric cm[1,1]
         RMSE
c1  .21931854
debruin_cbcount

symmetric cm[1,1]
         RMSE
c1  .19744711

symmetric cm[1,1]
         RMSE
c1  .21348792
debruin_ha_cbcount

symmetric cm[1,1]
         RMSE
c1  .19744711

symmetric cm[1,1]
         RMSE
c1  .21321694
lpop

symmetric cm[1,1]
         RMSE
c1  .19710579

symmetric cm[1,1]
         RMSE
c1  .19991233
xpers2

symmetric cm[1,1]
         RMSE
c1  .19695354

symmetric cm[1,1]
         RMSE
c1  .19674102

.                  
.                         * Change in RMSE from baseline *
.                         gen ch = (((rmse)-(base))/(rmse))*100
(4,516 missing values generated)

.                         * Plot change in RMSE *
.                         sort ch

.                         gen xn=_n

.                         replace n = xn
(4,548 real changes made)

.                         drop xn

.                         list n varname ch if n<=43,clean noobs

     n               varname          ch  
     1              election   -.3255014  
     2              lnregion   -.2615063  
     3                xpers2   -.1080204  
     4   l1v2x_freexp_altinf   -.0963443  
     5           l1v2x_clpol   -.0898256  
     6               legcomp   -.0830585  
     7                    lt   -.0249053  
     8       l1v2x_polyarchy   -.0078229  
     9            monoethnic   -.0002345  
    10           multiethnic    .0424867  
    11   l1v2x_frassoc_thick    .0450489  
    12                civwar    .0511113  
    13       l1v2x_partipdem    .1077999  
    14               support    .1775944  
    15           l1v2juhcind    .1932261  
    16           l1v2x_jucon    .1960899  
    17                loggdp    .1979711  
    18            l1v2clkill    .2417909  
    19        milethnic_homo    .2438331  
    20          lag_xongoing     .269619  
    21          l1v2x_libdem    .2887257  
    22            l1v2cltort    .4288887  
    23                logoil    .5813351  
    24          e_v2x_neopat    .6524837  
    25             leadermil    .6749457  
    26              excluded    .6979384  
    27      lag_milpersonnel    .9306849  
    28                  grow    1.050865  
    29                coup12    1.123148  
    30           lag_repress    1.228391  
    31                  lpop     1.40388  
    32          lag_milspend    1.713905  
    33            polparcomp     2.85624  
    34            polexconst    2.896177  
    35            polpolcomp    2.903621  
    36          l1v2cademmob    3.227572  
    37           wdipopurbmi    3.585442  
    38          l1poldurable    3.637101  
    39               ythbul4    4.021171  
    40              wditrade    5.413105  
    41    debruin_ha_cbcount    7.396147  
    42       debruin_cbcount    7.513688  
    43       effectivenumber    9.496707  

.                 
.                                 
.                                 *** Condition of baseline specification covari
> ates ***
.                                 global varA= "lag_xongoing l1v2cademmob loggdp
>  logoil support   leadermil coup12 election ythbul4 wdipopurbmi wditrade"

.                                 global varB= "polparcomp polpolcomp polexconst
>  l1v2x_polyarchy l1v2x_libdem l1v2x_partipdem l1v2x_freexp_altinf l1v2x_frasso
> c_thick "

.                                 global varC= "l1v2x_clpol l1v2x_jucon l1v2juhc
> ind e_v2x_neopat   legcomp excluded monoethnic  multiethnic civwar grow"

.                                 global varD= "l1v2cltort l1v2clkill lag_repres
> s l1poldurable milethnic_homo lag_milpersonnel lag_milspend effectivenumber de
> bruin_cbcount debruin_ha_cbcount lpop"      

.                                 local var = "$varA $varB $varC $varD xpers2"  
>   

.                                 local i =1

.                                 foreach v of local var {
  2.                                         qui set seed $seed
  3.                                         qui crossfold reghdfe $y xyrs* lnre
> gion lt if `v'~=.,a(gwf_caseid coldwar)  k($k) 
  4.                                         qui crossvalrmse
  5.                                         local r = cm[1,1]
  6.                                         qui replace base = `r'   if n==`i'
  7.                                         qui set seed $seed
  8.                                         qui crossfold reghdfe $y xyrs* lnre
> gion lt `v',a(gwf_caseid coldwar)  k($k) 
  9.                                         qui crossvalrmse
 10.                                         local r = cm[1,1]
 11.                                         qui replace rmse = `r'   if n==`i'
 12.                                         qui replace varname = "`v'" if n==`
> i'
 13.                                         local i = `i' +1
 14.                                 }

.                                 replace ch = (((rmse)-(base))/(rmse))*100
(41 real changes made)

.                                 sort ch

.                                 replace n =_n
(4,550 real changes made)

.                                 label define vlab 1 "Election" 2  "{bf:Securit
> y personalism}" 3 "Monoethnic party"  ///
>                                         4 "Legislative competitiveness" 5 "Mul
> tiethnic party" 6 "V-Civil liberties" 7 "V-Free expression info" ///
>                                         8 "Civil conflict" 9 "V-Free expressio
> n orgs" 10 "V-Polyarchy"  11 "Log GDPpc" 12 "Support party" ///
>                                         13 "V-Participation"  14 "Ethnically h
> omogenous military" 15 "V-Judical constraint"  ///
>                                         16 "V-Kill" 17 "V-Judicial independenc
> e" 18 "Ongoing protest campaign" 19 "V-Liberal democracy" ///
>                                         20 "V-Torture" 21 "Oil per capita (log
> )" 22 "Military leader" 23 "Excluded pop" 24 "V-Neopatrimonialism" ///
>                                         25 "Recent coup" 26 "Economic growth" 
> 27 "State repression" 28 "Military spending (log)" ///
>                                         29 "Military personnel (log)" 30"Popul
> ation" 31 "Parcomp" 32 "Polcomp"  33 "Executive constraint"   ///
>                                         34 "Prior pro-democracy mobilization" 
> 35 "Polity durable" 36 "Urban population" ///
>                                         37 "Youth buldge"  38 "Trade"  39 "Cou
> nter-weights" 40 "Heavily armed counter-weights" ///
>                                         41 "Effective # military organizations
> ",replace

.                                 label values n vlab,

.                                 list varname ch if n<=41,clean noobs

                varname          ch  
               election   -.3508669  
                 xpers2   -.2301674  
            l1v2x_clpol   -.0493606  
                legcomp   -.0342165  
    l1v2x_freexp_altinf   -.0341628  
             monoethnic   -.0305317  
        l1v2x_polyarchy    .0124717  
            multiethnic    .0244195  
                 civwar    .0347394  
    l1v2x_frassoc_thick    .0417338  
            l1v2x_jucon    .1189319  
        l1v2x_partipdem    .1195033  
                support    .1525835  
         milethnic_homo    .1803103  
            l1v2juhcind     .202779  
                 loggdp    .2042583  
           lag_xongoing    .2213591  
           l1v2x_libdem    .2645318  
             l1v2clkill    .2670074  
             l1v2cltort    .3644932  
                 logoil    .5669444  
           e_v2x_neopat    .5672979  
               excluded    .7068346  
              leadermil     .738267  
                 coup12    1.116586  
            lag_repress    1.188877  
                   grow    1.216487  
       lag_milpersonnel    1.775545  
           lag_milspend    1.866616  
                   lpop    2.550181  
             polparcomp     3.02765  
             polpolcomp    3.059693  
             polexconst    3.064358  
            wdipopurbmi    3.467193  
           l1v2cademmob    3.639958  
           l1poldurable    3.640845  
                ythbul4    3.957459  
               wditrade    4.877819  
     debruin_ha_cbcount    6.127532  
        debruin_cbcount    6.225989  
        debruin_cbcount    7.513688  

.                                 twoway (scatter n ch if n<=41,ysize(10)msym(P)
> mcol(blue)xtitle("Change in RMSE") ///
>                                         ytitle("",height(0)) xlab(0 (2) 10)  /
> //
>                                         ylab(1(1)41,valuelabel)legend(off)xlin
> e(0,lcol(red)))
(note:  named style P not found in class symbol, default attributes used)

.                                 graph export "$dir\rsme-onset.pdf", as(pdf)   
> replace
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\rsme-onset.pdf saved as PDF format

.                                 
.                         ******************************************************
> ***
.                         *** Cross-Validation with 10 runs of 5-folds          
>     ***
.                         *** for 41 variables added separately to the baseline 
> ***
.                         ******************************************************
> ***       
.                                 use temp-fe,clear

.                                 drop groups

.                                         gen l1poldurable = l.poldurable
(526 missing values generated)

.                                         gen l1polparcomp = l.polparcomp
(517 missing values generated)

.                                         gen l1polpolcomp = l.polpolcomp
(517 missing values generated)

.                                         gen l1polexconst = l.polexconst
(517 missing values generated)

.                                         gen l1v2x_libdem=l.v2x_libdem
(180 missing values generated)

.                                         gen l1ythbul4 = l.ythbul4
(616 missing values generated)

.                                         gen l1v2cltort=l1.v2cltort
(177 missing values generated)

.                                         gen l1v2clkill=l1.v2clkill
(177 missing values generated)

.                                         gen l1wdipopurbmi = l1.wdipopurbmi
(753 missing values generated)

.                                         gen l1wditrade = l1.wditrade
(1,612 missing values generated)

.                                         gen l1loggdp = l1.loggdp
(246 missing values generated)

.                                         gen l1lpop = l1.lpop
(222 missing values generated)

.                                 global s = 97454

.                                 global y="xonset"

.                                 global x="lxyrs lt lnregion coldwar"

.                                 global k="5"

.                                 global varA= "xpers2 l1v2cademmob l1loggdp l1l
> pop election l1ythbul4 lag_milpersonnel lag_milspend l1wdipopurbmi l1wditrade 
> "

.                                 global varB= "lag_xongoing l1polparcomp l1polp
> olcomp l1polexconst l1v2x_polyarchy l1v2x_libdem"

.                                 global varC= "l1v2x_partipdem l1v2x_freexp_alt
> inf l1v2x_frassoc_thick l1v2x_clpol l1v2x_jucon l1v2juhcind"

.                                 global varD= "e_v2x_neopat legcomp excluded mo
> noethnic multiethnic civwar grow l1v2cltort l1v2clkill lag_repress"

.                                 global varE= "l1poldurable milethnic_homo  eff
> ectivenumber debruin_cbcount debruin_ha_cbcount logoil support leadermil coup1
> 2"

.                                 
.                                 gen auc0=.
(4,559 missing values generated)

.                                 gen auc1=.
(4,559 missing values generated)

.                                 gen ratio=.
(4,559 missing values generated)

.                                 gen varname=""
(4,559 missing values generated)

.                                 gen n=_n

.                                 local var = "$varA $varB $varC $varD $varE"   
>   

.                                 local i =1

.                                 foreach v of local var {
  2.                                     di "`v'"
  3.                                         qui cvauroc $y $x if `v'~=.,k($k)se
> ed($s)
  4.                                         local r = r(mean_auc)
  5.                                         qui replace auc0 = `r' if n==`i'
  6.                                         qui cvauroc $y $x `v' if `v'~=.,k($
> k)seed($s)
  7.                                         local r = r(mean_auc)
  8.                                         qui replace auc1 = `r' if n==`i'
  9.                                         qui replace varname = "`v'" if n==`
> i'
 10.                                         qui xtset gwf_caseid year
 11.                                         qui xtsum `v'
 12.                                         qui replace ratio = r(sd_w)/(r(sd_w
> )+r(sd_b)) if n==`i'
 13.                                         local i = `i' +1
 14.                                 }
xpers2
l1v2cademmob
l1loggdp
l1lpop
election
l1ythbul4
lag_milpersonnel
lag_milspend
l1wdipopurbmi
l1wditrade
lag_xongoing
l1polparcomp
l1polpolcomp
l1polexconst
l1v2x_polyarchy
l1v2x_libdem
l1v2x_partipdem
l1v2x_freexp_altinf
l1v2x_frassoc_thick
l1v2x_clpol
l1v2x_jucon
l1v2juhcind
e_v2x_neopat
legcomp
excluded
monoethnic
multiethnic
civwar
grow
l1v2cltort
l1v2clkill
lag_repress
l1poldurable
milethnic_homo
effectivenumber
debruin_cbcount
debruin_ha_cbcount
logoil
support
leadermil
coup12

.                                 gen ch = (((auc1)-(auc0))/(auc0))*100
(4,518 missing values generated)

.                                 sort ch

.                                 replace n =_n
(4,559 real changes made)

.                                 browse n varname auc0 auc1 ch 

.                                 twoway (scatter ch ratio) (lpoly ch ratio)

.                                 
.                                 replace varname ="{bf:Security Personalism}" i
> f varname=="xpers2"
variable varname was str19 now str25
(1 real change made)

.                                 replace varname ="State repression" if varname
> =="lag_repress"
(1 real change made)

.                                 replace varname ="Election" if varname=="nelda
> _election"
(0 real changes made)

.                                 replace varname ="Polity durable" if varname==
> "l1poldurable"
(1 real change made)

.                                 replace varname ="V-Polyarchy" if varname=="l1
> v2x_polyarchy"
(1 real change made)

.                                 replace varname ="V-Civil liberties" if varnam
> e=="l1v2x_clpol"
(1 real change made)

.                                 replace varname ="Intnl conflict" if varname==
> "prio_lconflict_int_inter"
(0 real changes made)

.                                 replace varname ="Civil conflict" if varname==
> "prio_lconflict_int_intra"
(0 real changes made)

.                                 replace varname ="Ethnic military" if varname=
> ="milethnic_homo"
(1 real change made)

.                                 replace varname ="GDPpc" if varname=="l1loggdp
> "
(1 real change made)

.                                 replace varname ="Counter balance" if varname=
> ="debruin_affcount"
(0 real changes made)

.                                 replace varname ="# Security orgs" if varname=
> ="effectivenumber"
(1 real change made)

.                                 replace varname ="Youth bulge" if varname=="l1
> ythbul4"
(1 real change made)

.                                 replace varname ="Population" if varname=="l1l
> pop"
(1 real change made)

.                                 replace varname ="Urban population" if varname
> =="l1wdipopurbmi"
(1 real change made)

.                                 replace varname ="Support party" if varname=="
> support"
(1 real change made)

.                                 replace varname ="Excluded pop" if varname=="e
> xcluded"
(1 real change made)

.                                 replace varname ="Prior coup" if varname=="cou
> p12"
(1 real change made)

.                                 replace varname ="Oil per capita (log)" if var
> name=="logoil"
(1 real change made)

.                                 replace varname ="Legislative competitiveness"
>  if varname=="legcomp"
variable varname was str25 now str27
(1 real change made)

.                                 replace varname = "Multiethnic party" if varna
> me=="multiethnic"
(1 real change made)

.                                 replace varname = "Monoethnic party" if varnam
> e=="monoethnic"
(1 real change made)

.                                 replace varname = "Trade" if varname=="l1wditr
> ade"
(1 real change made)

.                                 replace varname = "Military personnel" if varn
> ame=="lag_milpersonnel"
(1 real change made)

.                                 replace varname = "Military spending" if varna
> me=="lag_milspend"
(1 real change made)

.                                 replace varname = "Prior pro-democracy mobiliz
> ation" if varname=="l1v2cademmob"
variable varname was str27 now str32
(1 real change made)

.                                 replace varname = "Election" if varname=="elec
> tion"
(1 real change made)

.                                 replace varname = "V-Free expression info" if 
> varname=="l1v2x_freexp_altinf"
(1 real change made)

.                                 replace varname = "Military leader" if varname
> =="leadermil"
(1 real change made)

.                                 replace varname = "V-Free expression orgs" if 
> varname=="l1v2x_frassoc_thick"
(1 real change made)

.                                 replace varname = "Counter-weights" if varname
> =="debruin_cbcount"
(1 real change made)

.                                 replace varname = "Heavily armed counter-weigh
> ts" if varname=="debruin_ha_cbcount"
(1 real change made)

.                                 replace varname = "Economic growth" if varname
> =="grow"
(1 real change made)

.                                 replace varname = "Polity parcomp" if varname=
> ="l1polparcomp"
(1 real change made)

.                                 replace varname = "Polity polcomp" if varname=
> ="l1polpolcomp"
(1 real change made)

.                                 replace varname = "Polity exconst" if varname=
> ="l1polexconst"
(1 real change made)

.                                 replace varname = "V-Neopatrimonialism" if var
> name=="e_v2x_neopat"
(1 real change made)

.                                 replace varname = "V-Participation" if varname
> =="l1v2x_partipdem"
(1 real change made)

.                                 replace varname = "Prior ongoing protest" if v
> arname=="lag_xongoing"
(1 real change made)

.                                 replace varname = "Civil war" if varname=="civ
> war"
(1 real change made)

.                                 replace varname = "V-Judicial independence" if
>  varname=="l1v2juhcind"
(1 real change made)

.                                 replace varname = "V-Judical constraint" if va
> rname=="l1v2x_jucon"
(1 real change made)

.                                 replace varname = "State torture" if varname==
> "l1v2cltort"
(1 real change made)

.                                 replace varname = "State killing" if varname==
> "l1v2clkill"
(1 real change made)

.                                 replace varname = "V-Liberal democracy" if var
> name=="l1v2x_libdem"               
(1 real change made)

.                                 twoway (scatter n ch if n>40 & n<=41,mlab(varn
> ame)mlabpos(left)msym(P)mcol(blue)) ///
>                                         (scatter n ch if n<=40,mlab(varname)xs
> cale(range(-4 12.5)) ///
>                                         msym(P)mcol(blue)xlab(-4(4)12)legend(o
> ff)ylab(1(1)41,  ///
>                                         labsize(tiny)valuelabel)ytitle(Added v
> ariables,size(small)) xline(0,lcol(red))  ///
>                                         xtitle(Percent change in baseline test
>  AUC,size(small)) title("Protest campaign onset")   ///
>                                         text(6 9 "{it:Baseline AUC: 0.614}", s
> ize(small)) ysize(8))
(note:  named style P not found in class symbol, default attributes used)
(note:  named style P not found in class symbol, default attributes used)

.                                 graph export "$dir\crossval-onset.pdf", as(pdf
> )   replace
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\crossval-onset.pdf saved as PDF format

.         
.                                 * Show that military variables don't increase 
> AUC substantially when population is in the specification *
.                                 local var ="lag_milsp lag_milper"

.                                 foreach v of local var {
  2.                                         qui cvauroc $y $x lpop if `v'~=.,k(
> $k)seed($s)
  3.                                         local r1 = r(mean_auc)
  4.                                         qui cvauroc $y $x `v' lpop if `v'~=
> .,k($k)seed($s)
  5.                                         local r2 = r(mean_auc)
  6.                                         local ch = (`r2'-`r1')/(`r1')
  7.                                         local s ="  "
  8.                                         di "`v'" 
  9.                                         di `r1'  
 10.                                         di `r2' 
 11.                                         di `ch'
 12.                                 }
lag_milsp
.67043307
.67734599
.01031113
lag_milper
.66616734
.66413143
-.00305616

.                         
.                         
. 
.                                 
.         ******************************************************************
.         **************************  REPRESSION  **************************
.         ******************************************************************
.                 ***** Differenced repression model *****
.                 use temp-fe,clear

.                 xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                 gen ongXpers=xongoing*xpers2

.                 gen onsXpers = xonset*xpers2

.                 xi:reghdfe d.repress d.xongoing d.xpers2,absorb(period* lt) cl
> uster(gwf_caseid)
(warning: absorbing 13 dimensions of fixed effects; check that you really want t
> hat)
(dropped 10 singleton observations)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =      4,184
Absorbing 13 HDFE groups                          F(   2,    251) =       3.43
Statistics robust to heteroskedasticity           Prob > F        =     0.0339
                                                  R-squared       =     0.0220
                                                  Adj R-squared   =     0.0078
                                                  Within R-sq.    =     0.0020
Number of clusters (gwf_caseid) =        252      Root MSE        =     0.1451

                           (Std. err. adjusted for 252 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
D.repression | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    xongoing |
         D1. |   .0228781   .0102027     2.24   0.026     .0027843     .042972
             |
      xpers2 |
         D1. |   .0096808   .0072639     1.33   0.184    -.0046251    .0239867
             |
       _cons |  -.0021684    .003387    -0.64   0.523     -.008839    .0045023
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     period1 |         2           0           2     |
     period2 |         2           1           1     |
     period3 |         2           1           1    ?|
     period4 |         2           1           1    ?|
     period5 |         2           1           1    ?|
     period6 |         2           1           1    ?|
     period7 |         2           1           1    ?|
     period8 |         2           1           1    ?|
     period9 |         2           1           1    ?|
    period10 |         2           1           1    ?|
    period11 |         2           1           1    ?|
    period12 |         2           1           1    ?|
          lt |        47           1          46    ?|
-----------------------------------------------------+
? = number of redundant parameters may be higher

.                 est store rd1

.                 xi:reghdfe d.repress d.xongoing d.xpers2 d.ongXpers,absorb(per
> iod* lt) cluster(gwf_caseid)
(warning: absorbing 13 dimensions of fixed effects; check that you really want t
> hat)
(dropped 10 singleton observations)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =      4,184
Absorbing 13 HDFE groups                          F(   3,    251) =       2.75
Statistics robust to heteroskedasticity           Prob > F        =     0.0435
                                                  R-squared       =     0.0234
                                                  Adj R-squared   =     0.0090
                                                  Within R-sq.    =     0.0034
Number of clusters (gwf_caseid) =        252      Root MSE        =     0.1451

                           (Std. err. adjusted for 252 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
D.repression | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    xongoing |
         D1. |   .0267754   .0105577     2.54   0.012     .0059824    .0475684
             |
      xpers2 |
         D1. |   .0061766   .0074464     0.83   0.408    -.0084887     .020842
             |
    ongXpers |
         D1. |   .0209913   .0107525     1.95   0.052    -.0001854    .0421679
             |
       _cons |  -.0021396   .0033953    -0.63   0.529    -.0088266    .0045474
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     period1 |         2           0           2     |
     period2 |         2           1           1     |
     period3 |         2           1           1    ?|
     period4 |         2           1           1    ?|
     period5 |         2           1           1    ?|
     period6 |         2           1           1    ?|
     period7 |         2           1           1    ?|
     period8 |         2           1           1    ?|
     period9 |         2           1           1    ?|
    period10 |         2           1           1    ?|
    period11 |         2           1           1    ?|
    period12 |         2           1           1    ?|
          lt |        47           1          46    ?|
-----------------------------------------------------+
? = number of redundant parameters may be higher

.                 est store rd2

.                 centile xpers2 if e(sample)==1,centile(10 90)

                                                          Binom. interp.   
    Variable |       Obs  Percentile    Centile        [95% conf. interval]
-------------+-------------------------------------------------------------
      xpers2 |     4,184         10   -1.551618       -1.551618   -1.551618
             |                   90    1.219388        1.219388    1.219388

.                 lincom d.xongoing + d.ongX*-1.6

 ( 1)  D.xongoing - 1.6*D.ongXpers = 0

------------------------------------------------------------------------------
D.repression | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0068107   .0148551    -0.46   0.647    -.0360672    .0224458
------------------------------------------------------------------------------

.                 lincom d.xongoing + d.ongX*1.3

 ( 1)  D.xongoing + 1.3*D.ongXpers = 0

------------------------------------------------------------------------------
D.repression | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    .054064   .0214151     2.52   0.012     .0118878    .0962403
------------------------------------------------------------------------------

.                 xi:reghdfe d.repress d.xongoing d.coupA d.coupS d.election d.c
> ivwar d.xpers2 lxyrs,absorb(period* lt) cluster(gwf_caseid)
(warning: absorbing 13 dimensions of fixed effects; check that you really want t
> hat)
(dropped 10 singleton observations)
(MWFE estimator converged in 6 iterations)

HDFE Linear regression                            Number of obs   =      4,149
Absorbing 13 HDFE groups                          F(   7,    250) =       4.00
Statistics robust to heteroskedasticity           Prob > F        =     0.0004
                                                  R-squared       =     0.0279
                                                  Adj R-squared   =     0.0124
                                                  Within R-sq.    =     0.0079
Number of clusters (gwf_caseid) =        251      Root MSE        =     0.1453

                           (Std. err. adjusted for 251 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
D.repression | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    xongoing |
         D1. |   .0198885   .0108045     1.84   0.067     -.001391     .041168
             |
       coupA |
         D1. |   .0222005   .0096694     2.30   0.023     .0031566    .0412444
             |
       coupS |
         D1. |   .0457444   .0136909     3.34   0.001     .0187802    .0727086
             |
    election |
         D1. |  -.0018833   .0072339    -0.26   0.795    -.0161304    .0123638
             |
      civwar |
         D1. |   .0275128   .0167995     1.64   0.103    -.0055738    .0605994
             |
      xpers2 |
         D1. |   .0119843   .0074014     1.62   0.107    -.0025927    .0265613
             |
       lxyrs |   .0004527   .0043417     0.10   0.917    -.0080983    .0090037
       _cons |  -.0030261   .0034544    -0.88   0.382    -.0098295    .0037773
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     period1 |         2           0           2     |
     period2 |         2           1           1     |
     period3 |         2           1           1    ?|
     period4 |         2           1           1    ?|
     period5 |         2           1           1    ?|
     period6 |         2           1           1    ?|
     period7 |         2           1           1    ?|
     period8 |         2           1           1    ?|
     period9 |         2           1           1    ?|
    period10 |         2           1           1    ?|
    period11 |         2           1           1    ?|
    period12 |         2           1           1    ?|
          lt |        47           1          46    ?|
-----------------------------------------------------+
? = number of redundant parameters may be higher

.                 est store rd3

.                 xi:reghdfe d.repress d.xongoing d.coupA d.coupS d.election d.c
> ivwar d.xpers2 d.ongXpers lxyrs,absorb(period* lt) cluster(gwf_caseid)
(warning: absorbing 13 dimensions of fixed effects; check that you really want t
> hat)
(dropped 10 singleton observations)
(MWFE estimator converged in 6 iterations)

HDFE Linear regression                            Number of obs   =      4,149
Absorbing 13 HDFE groups                          F(   8,    250) =       3.82
Statistics robust to heteroskedasticity           Prob > F        =     0.0003
                                                  R-squared       =     0.0293
                                                  Adj R-squared   =     0.0136
                                                  Within R-sq.    =     0.0093
Number of clusters (gwf_caseid) =        251      Root MSE        =     0.1453

                           (Std. err. adjusted for 251 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
D.repression | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    xongoing |
         D1. |    .023959   .0111473     2.15   0.033     .0020045    .0459135
             |
       coupA |
         D1. |   .0222951   .0096722     2.31   0.022     .0032458    .0413445
             |
       coupS |
         D1. |   .0455893   .0135782     3.36   0.001     .0188471    .0723315
             |
    election |
         D1. |  -.0020102   .0071879    -0.28   0.780    -.0161668    .0121463
             |
      civwar |
         D1. |     .02818   .0167636     1.68   0.094    -.0048359     .061196
             |
      xpers2 |
         D1. |   .0083771   .0075619     1.11   0.269    -.0065161    .0232703
             |
    ongXpers |
         D1. |   .0212593   .0105976     2.01   0.046     .0003873    .0421313
             |
       lxyrs |   .0003261   .0043335     0.08   0.940    -.0082087    .0088609
       _cons |   -.002978   .0034628    -0.86   0.391     -.009798     .003842
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     period1 |         2           0           2     |
     period2 |         2           1           1     |
     period3 |         2           1           1    ?|
     period4 |         2           1           1    ?|
     period5 |         2           1           1    ?|
     period6 |         2           1           1    ?|
     period7 |         2           1           1    ?|
     period8 |         2           1           1    ?|
     period9 |         2           1           1    ?|
    period10 |         2           1           1    ?|
    period11 |         2           1           1    ?|
    period12 |         2           1           1    ?|
          lt |        47           1          46    ?|
-----------------------------------------------------+
? = number of redundant parameters may be higher

.                 est store rd4

.                 lincom d.xongoing + d.ongX*-1.6

 ( 1)  D.xongoing - 1.6*D.ongXpers = 0

------------------------------------------------------------------------------
D.repression | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0100559   .0155171    -0.65   0.518    -.0406169    .0205051
------------------------------------------------------------------------------

.                 lincom d.xongoing + d.ongX*1.3

 ( 1)  D.xongoing + 1.3*D.ongXpers = 0

------------------------------------------------------------------------------
D.repression | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0515961   .0212837     2.42   0.016     .0096778    .0935144
------------------------------------------------------------------------------

. 
.                         ** Check split sample **
.                         xi:qui reghdfe d.repress d.xongoing d.coupA d.coupS d.
> election d.civwar lxyrs if xpers2~=.,absorb(period* lt) cluster(gwf_caseid)

.                         lincom d.xongoing

 ( 1)  D.xongoing = 0

------------------------------------------------------------------------------
D.repression | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0203197   .0108497     1.87   0.062    -.0010489    .0416882
------------------------------------------------------------------------------

.                         qui sum xpers2 if e(sample)==1,detail

.                         local m = r(p50)

.                         xi:qui reghdfe d.repress d.xongoing d.coupA d.coupS d.
> election d.civwar lxyrs if xpers2>=`m',absorb(period* lt) cluster(gwf_caseid)

.                         lincom d.xongoing       

 ( 1)  D.xongoing = 0

------------------------------------------------------------------------------
D.repression | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0410745   .0182524     2.25   0.026     .0050262    .0771229
------------------------------------------------------------------------------

.                         est store rd5

.                         xi:qui reghdfe d.repress d.xongoing d.coupA d.coupS d.
> election d.civwar lxyrs if xpers2<`m',absorb(period* lt) cluster(gwf_caseid)

.                         lincom d.xongoing

 ( 1)  D.xongoing = 0

------------------------------------------------------------------------------
D.repression | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0088607   .0122741     0.72   0.471    -.0153728    .0330942
------------------------------------------------------------------------------

.                         est store rd6

.                         
.                 * Differenced estimates table *
.                 estout rd1 rd2 rd3 rd4 rd5 rd6 using TableC1.tex, cells(b(star
>   fmt(%9.4f)) se(par fmt(%9.3f))) ///
>                         stats(r2 N N_clust) style(tex) replace label starlevel
> s(* 0.05) title(\label{tabC1})
(file TableC1.tex not found)
(output written to TableC1.tex)

.                         
.                 * Additional FEs *
.                 xi:reg d.repress d.xongoing d.coupA d.coupS d.election d.civwa
> r d.xpers2 d.ongXpers lt, cluster(gwf_caseid)

Linear regression                               Number of obs     =      4,159
                                                F(8, 250)         =       4.64
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0111
                                                Root MSE          =       .146

                           (Std. err. adjusted for 251 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
D.repression | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    xongoing |
         D1. |   .0246433   .0105273     2.34   0.020     .0039097    .0453768
             |
       coupA |
         D1. |   .0227367   .0096598     2.35   0.019     .0037117    .0417617
             |
       coupS |
         D1. |   .0528179   .0138913     3.80   0.000     .0254589    .0801768
             |
    election |
         D1. |  -.0029717   .0072561    -0.41   0.682    -.0172625    .0113191
             |
      civwar |
         D1. |    .028163   .0167513     1.68   0.094    -.0048286    .0611546
             |
      xpers2 |
         D1. |    .011253   .0074951     1.50   0.135    -.0035085    .0260145
             |
    ongXpers |
         D1. |   .0204711    .010477     1.95   0.052    -.0001634    .0411056
             |
          lt |  -.0014873   .0040274    -0.37   0.712    -.0094192    .0064446
       _cons |  -.0024922   .0036406    -0.68   0.494    -.0096623     .004678
------------------------------------------------------------------------------

.                 xi:reghdfe d.repress d.xongoing d.coupA d.coupS d.election d.c
> ivwar d.xpers2 d.ongXpers lt,absorb(year cow) cluster(gwf_caseid)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =      4,159
Absorbing 2 HDFE groups                           F(   8,    250) =       3.71
Statistics robust to heteroskedasticity           Prob > F        =     0.0004
                                                  R-squared       =     0.0880
                                                  Adj R-squared   =     0.0460
                                                  Within R-sq.    =     0.0104
Number of clusters (gwf_caseid) =        251      Root MSE        =     0.1433

                           (Std. err. adjusted for 251 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
D.repression | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    xongoing |
         D1. |   .0270508   .0104837     2.58   0.010     .0064032    .0476985
             |
       coupA |
         D1. |   .0216201   .0095021     2.28   0.024     .0029057    .0403345
             |
       coupS |
         D1. |   .0473021   .0138202     3.42   0.001     .0200831     .074521
             |
    election |
         D1. |  -.0012318   .0072641    -0.17   0.865    -.0155385    .0130749
             |
      civwar |
         D1. |   .0254674   .0173357     1.47   0.143    -.0086753    .0596101
             |
      xpers2 |
         D1. |   .0047931   .0078242     0.61   0.541    -.0106167    .0202029
             |
    ongXpers |
         D1. |   .0219124   .0108517     2.02   0.045       .00054    .0432847
             |
          lt |   .0011863   .0041939     0.28   0.778    -.0070736    .0094462
       _cons |  -.0026351   .0023163    -1.14   0.256    -.0071971    .0019269
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        60           0          60     |
     cowcode |       117           1         116     |
-----------------------------------------------------+

.                 xi:reghdfe d.repress d.xongoing d.coupA d.coupS d.election d.c
> ivwar d.xpers2 d.ongXpers lt,absorb(year gwf_caseid) cluster(gwf_caseid)
(dropped 22 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =      4,137
Absorbing 2 HDFE groups                           F(   8,    228) =       3.26
Statistics robust to heteroskedasticity           Prob > F        =     0.0015
                                                  R-squared       =     0.1840
                                                  Adj R-squared   =     0.1211
                                                  Within R-sq.    =     0.0090
Number of clusters (gwf_caseid) =        229      Root MSE        =     0.1375

                           (Std. err. adjusted for 229 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
D.repression | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    xongoing |
         D1. |   .0264496   .0104504     2.53   0.012     .0058579    .0470412
             |
       coupA |
         D1. |   .0201746    .009353     2.16   0.032     .0017452    .0386041
             |
       coupS |
         D1. |   .0435647   .0133689     3.26   0.001     .0172224     .069907
             |
    election |
         D1. |    .001375   .0071126     0.19   0.847    -.0126398    .0153899
             |
      civwar |
         D1. |   .0215324   .0168654     1.28   0.203    -.0116996    .0547644
             |
      xpers2 |
         D1. |   .0004506   .0078568     0.06   0.954    -.0150307    .0159318
             |
    ongXpers |
         D1. |    .021033   .0105505     1.99   0.047     .0002442    .0418219
             |
          lt |  -.0009632   .0046162    -0.21   0.835    -.0100591    .0081327
       _cons |  -.0020306   .0006444    -3.15   0.002    -.0033004   -.0007609
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        60           0          60     |
  gwf_caseid |       229         229           0    *|
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.                 xtset gwf_leaderid year

Panel variable: gwf_leaderid (unbalanced)
 Time variable: year, 1946 to 2010, but with gaps
         Delta: 1 unit

.                 xi:reghdfe d.repress d.xongoing d.coupA d.coupS d.election d.c
> ivwar d.xpers2 d.ongXpers lt,absorb(year) cluster(gwf_caseid)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =      3,929
Absorbing 1 HDFE group                            F(   8,    245) =       4.63
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0338
                                                  Adj R-squared   =     0.0170
                                                  Within R-sq.    =     0.0128
Number of clusters (gwf_caseid) =        246      Root MSE        =     0.1447

                           (Std. err. adjusted for 246 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
D.repression | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    xongoing |
         D1. |    .028675   .0111594     2.57   0.011     .0066945    .0506556
             |
       coupA |
         D1. |    .025651   .0104265     2.46   0.015      .005114    .0461881
             |
       coupS |
         D1. |   .0692756   .0168626     4.11   0.000     .0360614    .1024898
             |
    election |
         D1. |   .0008943   .0076898     0.12   0.908    -.0142523    .0160408
             |
      civwar |
         D1. |   .0207219   .0175074     1.18   0.238    -.0137622     .055206
             |
      xpers2 |
         D1. |   .0110851   .0109072     1.02   0.310    -.0103986    .0325689
             |
    ongXpers |
         D1. |   .0173829   .0110904     1.57   0.118    -.0044618    .0392276
             |
          lt |  -.0001296   .0044502    -0.03   0.977    -.0088952     .008636
       _cons |  -.0028182   .0035594    -0.79   0.429    -.0098292    .0041928
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        60           0          60     |
-----------------------------------------------------+

.                 lincom d.xongoing + d.ongX*-1.6

 ( 1)  D.xongoing - 1.6*D.ongXpers = 0

------------------------------------------------------------------------------
D.repression | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0008624   .0165687     0.05   0.959    -.0317728    .0334977
------------------------------------------------------------------------------

.                 lincom d.xongoing + d.ongX*1.3

 ( 1)  D.xongoing + 1.3*D.ongXpers = 0

------------------------------------------------------------------------------
D.repression | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0512728   .0215954     2.37   0.018     .0087364    .0938092
------------------------------------------------------------------------------

. 
.                 ***** Repression in onset year & during protest campaigns ****
> *
.                 use temp-fe,clear

.                 xtset gwf_caseid

Panel variable: gwf_caseid (unbalanced)

.                 egen mean_repress=mean(repress),by(gwf_caseid)
(14 missing values generated)

.                 egen mx_repress=mean(repress) if xongoing==0,by(gwf_caseid)
(346 missing values generated)

.                 egen m_repress=max(mx_),by(gwf_caseid)
(36 missing values generated)

.                 global x1 = "lpop lnregion lt lxyrs"

.                 centile xpers2 if xonset==1,centile(50)

                                                          Binom. interp.   
    Variable |       Obs  Percentile    Centile        [95% conf. interval]
-------------+-------------------------------------------------------------
      xpers2 |       182         50   -.2625257       -.3861327   -.0587348

.                 centile xpers2,centile(50)

                                                          Binom. interp.   
    Variable |       Obs  Percentile    Centile        [95% conf. interval]
-------------+-------------------------------------------------------------
      xpers2 |     4,559         50   -.0304208       -.1364154    .0329928

. 
.                 qui sum xpers2 if lpopl~=.,detail

.                 local m=r(p50)

.                 gen treat1=xpers2>=`m'           

.                  ciplot lag1repress if xonset==1,by(treat) xtitle(Security per
> sonalization,size(small)height(3)) ///
>                          xlab(2  "Low"  5  "High"  ) note("")  ///
>                          ytitle("Lagged repression",height(2)) title("Repressi
> on{sub:t-1}") saving(r1.gph,replace)
file r1.gph saved

.                  ciplot lag2repress if xonset==1,by(treat) xtitle(Security per
> sonalization,size(small)height(3)) ///
>                          xlab(2  "Low"  5  "High"  ) note("")  ///
>                          ytitle("Lagged repression",height(2)) title("Repressi
> on{sub:t-2}") saving(r2.gph,replace)
file r2.gph saved

.                  ciplot lag3repress if xonset==1,by(treat) xtitle(Security per
> sonalization,size(small)height(3)) ///
>                          xlab(2  "Low"  5  "High"  ) note("")  ///
>                          ytitle("Lagged repression",height(2)) title("Repressi
> on{sub:t-3}") saving(r3.gph,replace)
(file r3.gph not found)
file r3.gph saved

.                  ciplot lag_repress if xonset==1,by(treat) xtitle(Security per
> sonalization,size(small)height(3)) ///
>                          xlab(2  "Low"  5  "High"  ) note("")  ///
>                          ytitle("Lagged repression",height(2)) title("Repressi
> on{sub:t-1:t-3}") saving(r4.gph,replace)
(file r4.gph not found)
file r4.gph saved

.                 gr combine r1.gph r2.gph r3.gph r4.gph, ycommon col(2) iscale(
> .7) ysize(5) xsize(5)

.                 graph export "$dir\repression-ttests.pdf", as(pdf)   replace
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\repression-ttests.pdf saved as PDF format

. 
.                 gen beta = .
(4,559 missing values generated)

.                 gen n =_n

.                 gen hi = .
(4,559 missing values generated)

.                 gen lo=.
(4,559 missing values generated)

.                 
.                 krls repress xpers2 $x1 period* lag1repress lag2repress lag3re
> press if xonset==1  
Iteration =  1, Looloss: 134.4992  
Iteration =  2, Looloss: 126.914   
Iteration =  3, Looloss: 116.5152  
Iteration =  4, Looloss: 103.3642  
Iteration =  5, Looloss: 88.39393  
Iteration =  6, Looloss: 73.34533  
Iteration =  7, Looloss: 60.04733  
Iteration =  8, Looloss: 49.53344  
Iteration =  9, Looloss: 41.79686  
Iteration = 10, Looloss: 36.24015  
Iteration = 11, Looloss: 32.2087   
Iteration = 12, Looloss: 29.22224  
Iteration = 13, Looloss: 26.97606  
Iteration = 14, Looloss: 25.28592  
Iteration = 15, Looloss: 24.03987  
Iteration = 16, Looloss: 23.15741  
Iteration = 17, Looloss: 22.56275  
Iteration = 18, Looloss: 22.18014  

Pointwise Derivatives                                     Number of obs =      1
> 79 
                                                          Lambda        =    .06
> 11 
                                                          Tolerance     =     .1
> 79 
                                                          Sigma         =       
> 19 
                                                          Eff. df       =    119
> .2 
                                                          R2            =     .9
> 88 
                                                          Looloss       =     21
> .8

  repression |      Avg.       SE        t    P>|t|        P25       P50       P
> 75       
-------------+------------------------------------------------------------------
> --
      xpers2 |  .039377   .008775    4.488    0.000   -.008026   .034551   .0905
> 35  
       lpopl |  .031612   .006919    4.569    0.000   -.003909   .029589   .0769
> 58  
    lnregion | -.017725   .006925   -2.560    0.011   -.061745  -.012324   .0263
> 61  
          lt |  -.02787   .008812   -3.163    0.002    -.07987  -.028061   .0213
> 72  
       lxyrs |  .000706   .008197    0.086    0.931   -.030865   -.00047   .0347
> 29  
    *period2 | -.054353   .028654   -1.897    0.060   -.264839  -.050877   .1682
> 59  
    *period3 |   .00053   .031403    0.017    0.987   -.284362  -.011228   .2521
> 76  
    *period4 | -.010147   .031475   -0.322    0.748   -.173688  -.018672    .188
> 86  
    *period5 | -.126053   .031584   -3.991    0.000   -.459161  -.105962    .257
> 89  
    *period6 | -.031002   .030451   -1.018    0.310   -.207984  -.061947   .1382
> 42  
    *period7 | -.027382   .032458   -0.844    0.400   -.235064  -.032764   .1987
> 74  
    *period8 | -.065376   .032946   -1.984    0.049   -.275155  -.055464   .1378
> 83  
    *period9 |  .002749   .030796    0.089    0.929   -.255449   .017418   .2290
> 35  
   *period10 | -.066488   .029602   -2.246    0.026   -.253925   -.07309   .1592
> 31  
   *period11 |  .019929   .030751    0.648    0.518   -.188927   .063013    .268
> 16  
   *period12 | -.007725   .030658   -0.252    0.801   -.179936   .000499   .2096
> 81  
 lag1repress |  .708477   .019694   35.975    0.000    .618369   .723807   .8133
> 15  
 lag2repress |  .181927   .014681   12.392    0.000    .130755   .182915   .2298
> 66  
 lag3repress | -.074127    .01853   -4.000    0.000   -.145362  -.047375   .0033
> 65  
-------------+------------------------------------------------------------------
> --


.                         mat o=e(Output)

.                         replace beta = o[1,1] if n==3
(1 real change made)

.                         replace hi = o[1,1]+1.95*o[1,2]  if n==3
(1 real change made)

.                         replace lo = o[1,1]-1.95*o[1,2]  if n==3
(1 real change made)

.                 krls repress xpers2 $x1 period* lag1repress lag2repress if xon
> set==1
Iteration =  1, Looloss: 138.1597  
Iteration =  2, Looloss: 131.2923  
Iteration =  3, Looloss: 121.6544  
Iteration =  4, Looloss: 109.0575  
Iteration =  5, Looloss: 94.08286  
Iteration =  6, Looloss: 78.22417  
Iteration =  7, Looloss: 63.41009  
Iteration =  8, Looloss: 51.1147   
Iteration =  9, Looloss: 41.82216  
Iteration = 10, Looloss: 35.21421  
Iteration = 11, Looloss: 30.66601  
Iteration = 12, Looloss: 27.57644  
Iteration = 13, Looloss: 25.47256  
Iteration = 14, Looloss: 24.01827  
Iteration = 15, Looloss: 22.99783  
Iteration = 16, Looloss: 22.28275  
Iteration = 17, Looloss: 21.79239  
Iteration = 18, Looloss: 21.46688  

Pointwise Derivatives                                     Number of obs =      1
> 81 
                                                          Lambda        =   .061
> 23 
                                                          Tolerance     =     .1
> 81 
                                                          Sigma         =       
> 18 
                                                          Eff. df       =    118
> .3 
                                                          R2            =    .98
> 67 
                                                          Looloss       =    21.
> 13

  repression |      Avg.       SE        t    P>|t|        P25       P50       P
> 75       
-------------+------------------------------------------------------------------
> --
      xpers2 |  .032095    .00928    3.459    0.001   -.019379   .026936   .0834
> 17  
       lpopl |  .031781   .007262    4.376    0.000   -.001051   .032863   .0844
> 72  
    lnregion | -.014856   .007445   -1.995    0.048   -.062642  -.004762   .0324
> 41  
          lt | -.027413   .009366   -2.927    0.004   -.071747  -.027924   .0248
> 41  
       lxyrs |  .004841    .00881    0.550    0.583     -.0306    .00697   .0407
> 08  
    *period2 | -.059995   .030018   -1.999    0.047   -.274052  -.069172   .1843
> 18  
    *period3 |  .005808   .031453    0.185    0.854   -.270246  -.015406   .2864
> 16  
    *period4 | -.013024   .032676   -0.399    0.691   -.180295  -.029908   .1897
> 46  
    *period5 | -.129197   .032984   -3.917    0.000   -.481053  -.121663   .2541
> 36  
    *period6 | -.032525   .032215   -1.010    0.314    -.22661  -.065174   .1669
> 15  
    *period7 | -.023852   .033619   -0.709    0.479   -.244192  -.033402   .2111
> 63  
    *period8 | -.072413   .034893   -2.075    0.040   -.289772  -.067015   .1466
> 51  
    *period9 | -.001345   .032186   -0.042    0.967   -.263887   .010108   .2429
> 02  
   *period10 | -.064241   .030388   -2.114    0.036   -.268737  -.082122   .1735
> 65  
   *period11 |  .025462   .031629    0.805    0.422   -.205226   .056873   .2889
> 72  
   *period12 |  -.00247   .031556   -0.078    0.938   -.179511    .02424   .2261
> 62  
 lag1repress |  .727694   .019907   36.554    0.000    .643457   .746027   .8418
> 38  
 lag2repress |  .096689   .019622    4.928    0.000    .004807   .102446   .1941
> 71  
-------------+------------------------------------------------------------------
> --


.                         mat o=e(Output)

.                         replace beta = o[1,1] if n==2
(1 real change made)

.                         replace hi = o[1,1]+1.95*o[1,2]  if n==2
(1 real change made)

.                         replace lo = o[1,1]-1.95*o[1,2]  if n==2
(1 real change made)

.                 krls repress xpers2 $x1 period* lag1repress if xonset==1
Iteration =  1, Looloss: 141.5051  
Iteration =  2, Looloss: 136.2377  
Iteration =  3, Looloss: 128.6192  
Iteration =  4, Looloss: 118.2154  
Iteration =  5, Looloss: 105.0698  
Iteration =  6, Looloss: 89.98721  
Iteration =  7, Looloss: 74.4516   
Iteration =  8, Looloss: 60.07483  
Iteration =  9, Looloss: 47.97408  
Iteration = 10, Looloss: 38.55217  
Iteration = 11, Looloss: 31.66996  
Iteration = 12, Looloss: 26.90927  
Iteration = 13, Looloss: 23.76641  
Iteration = 14, Looloss: 21.77135  
Iteration = 15, Looloss: 20.54775  
Iteration = 16, Looloss: 19.82332  
Iteration = 17, Looloss: 19.41114  

Pointwise Derivatives                                     Number of obs =      1
> 81 
                                                          Lambda        =   .068
> 18 
                                                          Tolerance     =     .1
> 81 
                                                          Sigma         =       
> 17 
                                                          Eff. df       =    112
> .3 
                                                          R2            =    .98
> 47 
                                                          Looloss       =    19.
> 07

  repression |      Avg.       SE        t    P>|t|        P25       P50       P
> 75       
-------------+------------------------------------------------------------------
> --
      xpers2 |  .024383   .010009    2.436    0.016   -.020812   .019183   .0804
> 58  
       lpopl |  .035145   .007711    4.558    0.000    -.00937   .042228   .0815
> 73  
    lnregion | -.008456   .008086   -1.046    0.297   -.061756  -.003369   .0421
> 91  
          lt | -.026655   .010096   -2.640    0.009   -.080086  -.027661   .0243
> 54  
       lxyrs |  .003583   .009479    0.378    0.706   -.038155   .009843   .0511
> 83  
    *period2 | -.058042   .031659   -1.833    0.069   -.274091  -.085429   .2136
> 83  
    *period3 |  .015532   .032529    0.477    0.634   -.260284    .02364   .3388
> 99  
    *period4 | -.010697    .03417   -0.313    0.755   -.190434  -.027236   .2059
> 02  
    *period5 | -.132767   .034948   -3.799    0.000   -.496579  -.146175   .2720
> 86  
    *period6 |  -.03379    .03406   -0.992    0.323   -.228656  -.052082    .171
> 19  
    *period7 | -.011773   .035082   -0.336    0.738   -.224563  -.013648   .2467
> 61  
    *period8 | -.076284   .037008   -2.061    0.041   -.270113   -.07726   .1200
> 91  
    *period9 | -.003873    .03405   -0.114    0.910   -.253777  -.003456   .2527
> 27  
   *period10 | -.062666   .031833   -1.969    0.051   -.276723  -.077365   .1907
> 02  
   *period11 |  .022522   .033068    0.681    0.497   -.227479   .036035   .2985
> 24  
   *period12 | -.008052   .033058   -0.244    0.808   -.213203    .00686   .2284
> 53  
 lag1repress |  .826082   .014829   55.707    0.000    .703455   .847032   .9656
> 22  
-------------+------------------------------------------------------------------
> --


.                         mat o=e(Output)

.                         replace beta = o[1,1] if n==1
(1 real change made)

.                         replace hi = o[1,1]+1.95*o[1,2]  if n==1
(1 real change made)

.                         replace lo = o[1,1]-1.95*o[1,2]  if n==1
(1 real change made)

.                 krls repress xpers2 $x1 period* mean_repress if xonset==1 /*co
> ndition on average regime repression level in all years */
Iteration =  1, Looloss: 142.8802  
Iteration =  2, Looloss: 138.3992  
Iteration =  3, Looloss: 131.9458  
Iteration =  4, Looloss: 123.1793  
Iteration =  5, Looloss: 112.1815  
Iteration =  6, Looloss: 99.69722  
Iteration =  7, Looloss: 87.04806  
Iteration =  8, Looloss: 75.63407  
Iteration =  9, Looloss: 66.38198  
Iteration = 10, Looloss: 59.57114  
Iteration = 11, Looloss: 55.02356  
Iteration = 12, Looloss: 52.36455  

Pointwise Derivatives                                      Number of obs =      
> 181 
                                                           Lambda        =    .2
> 504 
                                                           Tolerance     =     .
> 181 
                                                           Sigma         =      
>  17 
                                                           Eff. df       =    76
> .73 
                                                           R2            =    .8
> 959 
                                                           Looloss       =    51
> .12

   repression |      Avg.       SE        t    P>|t|        P25       P50       
> P75       
--------------+-----------------------------------------------------------------
> ---
       xpers2 |  .095164   .021207    4.487    0.000    .016881   .104626   .172
> 586  
        lpopl |  .073497   .015811    4.648    0.000    .023833   .072738   .124
> 106  
     lnregion | -.035343   .017201   -2.055    0.041   -.086816  -.030966   .014
> 609  
           lt | -.009099   .021316   -0.427    0.670   -.058548  -.016544   .041
> 111  
        lxyrs |  .011678   .019404    0.602    0.548   -.065981   .030688   .082
> 185  
     *period1 | -.240046   .103962   -2.309    0.022   -.738345  -.245189   .196
> 517  
     *period2 |  .009122   .070462    0.129    0.897   -.232105   .014065   .302
> 462  
     *period3 |  .002527   .078305    0.032    0.974   -.312102    .02259   .288
> 127  
     *period4 |   .01635   .066719    0.245    0.807    -.10585   .006853   .151
> 605  
     *period6 |  .258081   .066712    3.869    0.000    .033426   .266418   .497
> 267  
     *period7 |  .059739   .074668    0.800    0.425   -.145066   .073123   .274
> 624  
     *period8 | -.030214   .064093   -0.471    0.638   -.186047  -.004079   .139
> 639  
     *period9 |  .016953   .075238    0.225    0.822   -.277224   .036436   .292
> 406  
    *period10 |    -.097   .063412   -1.530    0.128   -.293904  -.122876   .083
> 417  
    *period11 |  .061769   .069536    0.888    0.376   -.198565   .073301    .31
> 551  
    *period12 | -.070569   .065987   -1.069    0.286   -.219259  -.079201   .081
> 978  
 mean_repress |  .577261   .027263   21.174    0.000    .445436   .602837   .681
> 671  
--------------+-----------------------------------------------------------------
> ---


.                         mat o=e(Output)

.                         replace beta = o[1,1] if n==4
(1 real change made)

.                         replace hi = o[1,1]+1.95*o[1,2]  if n==4
(1 real change made)

.                         replace lo = o[1,1]-1.95*o[1,2]  if n==4
(1 real change made)

.                 krls repress xpers2 $x1 period* m_repress if xonset==1    /*co
> ndition on average regime repression level in non-NVC years */
Iteration =  1, Looloss: 137.4393  
Iteration =  2, Looloss: 133.3698  
Iteration =  3, Looloss: 127.4864  
Iteration =  4, Looloss: 119.4589  
Iteration =  5, Looloss: 109.3461  
Iteration =  6, Looloss: 97.83611  
Iteration =  7, Looloss: 86.18133  
Iteration =  8, Looloss: 75.71872  
Iteration =  9, Looloss: 67.31966  
Iteration = 10, Looloss: 61.22044  
Iteration = 11, Looloss: 57.22575  
Iteration = 12, Looloss: 54.97096  

Pointwise Derivatives                                   Number of obs =      174
>  
                                                        Lambda        =    .3608
>  
                                                        Tolerance     =     .174
>  
                                                        Sigma         =       17
>  
                                                        Eff. df       =    65.03
>  
                                                        R2            =    .8655
>  
                                                        Looloss       =    54.07

repression |      Avg.       SE        t    P>|t|        P25       P50       P75
>        
-----------+--------------------------------------------------------------------
    xpers2 |  .105977   .022656    4.678    0.000    .039036   .112087   .179789
>   
     lpopl |  .089696   .016455    5.451    0.000    .038209   .085908   .141654
>   
  lnregion |  -.03345   .018471   -1.811    0.072   -.075841  -.033003   .011264
>   
        lt | -.010769     .0242   -0.445    0.657   -.064004  -.015147   .032633
>   
     lxyrs |  .015158   .021262    0.713    0.477   -.051674   .028137   .086853
>   
  *period1 | -.218639   .108101   -2.023    0.045   -.673106  -.198209   .203065
>   
  *period2 |  .011273   .076958    0.146    0.884   -.227744   .048453   .290448
>   
  *period3 |  .015253   .085965    0.177    0.859   -.278454   .048895   .301956
>   
  *period4 |  .026325   .072052    0.365    0.715   -.110846   .021331   .165851
>   
  *period6 |  .268141    .07268    3.689    0.000    .011874   .285015   .523491
>   
  *period7 |  .053745     .0808    0.665    0.507   -.149868   .065884   .277473
>   
  *period8 | -.017324   .067539   -0.257    0.798   -.162136   .004368   .137347
>   
  *period9 |  .036299   .084807    0.428    0.669   -.258919   .091158   .317496
>   
 *period10 | -.105241   .069625   -1.512    0.133    -.29103  -.126199    .09207
>   
 *period11 |   .06453   .075209    0.858    0.392   -.158435    .06634   .313699
>   
 *period12 | -.077167   .072319   -1.067    0.288   -.241105  -.056343    .09387
>   
 m_repress |  .508782   .027956   18.199    0.000    .389678   .530667   .606615
>   
-----------+--------------------------------------------------------------------


.                         mat o=e(Output)

.                         replace beta = o[1,1] if n==5
(1 real change made)

.                         replace hi = o[1,1]+1.95*o[1,2]  if n==5
(1 real change made)

.                         replace lo = o[1,1]-1.95*o[1,2]  if n==5
(1 real change made)

.                 krls repress xpers2 $x1 period* if xonset==1                  
>           /*no condition on lagged repression */
Iteration =  1, Looloss: 147.7428  
Iteration =  2, Looloss: 145.8779  
Iteration =  3, Looloss: 143.164   
Iteration =  4, Looloss: 139.4363  
Iteration =  5, Looloss: 134.713   
Iteration =  6, Looloss: 129.3254  
Iteration =  7, Looloss: 123.919   
Iteration =  8, Looloss: 119.2515  
Iteration =  9, Looloss: 115.9     
Iteration = 10, Looloss: 114.105   
Iteration = 11, Looloss: 114.874   

Pointwise Derivatives                                   Number of obs =      181
>  
                                                        Lambda        =    1.099
>  
                                                        Tolerance     =     .181
>  
                                                        Sigma         =       16
>  
                                                        Eff. df       =    38.45
>  
                                                        R2            =    .5457
>  
                                                        Looloss       =    113.8

repression |      Avg.       SE        t    P>|t|        P25       P50       P75
>        
-----------+--------------------------------------------------------------------
    xpers2 |  .115941    .03061    3.788    0.000    .046726   .120371   .177208
>   
     lpopl |  .211067    .02076   10.167    0.000    .156661   .205365   .273368
>   
  lnregion | -.041231   .025828   -1.596    0.112   -.076686  -.032911   .001264
>   
        lt |  -.05207   .030807   -1.690    0.093   -.106165  -.050606  -.001851
>   
     lxyrs | -.018691    .02917   -0.641    0.523   -.074392  -.009431   .033143
>   
  *period2 |  .026284   .113927    0.231    0.818   -.209203  -.003642   .296181
>   
  *period3 |  .124522   .125099    0.995    0.321   -.134414   .115579   .407126
>   
  *period4 |  .063165   .099448    0.635    0.526    -.06046   .069978   .213664
>   
  *period5 | -.127646   .127797   -0.999    0.319   -.396334   -.12858   .154533
>   
  *period6 |  .264929   .102892    2.575    0.011    .105626   .250545   .427399
>   
  *period7 |  .201836    .10957    1.842    0.067    .009922   .205303   .402734
>   
  *period8 |  -.00924    .07978   -0.116    0.908   -.113844  -.016122   .089451
>   
  *period9 |  .056529   .111065    0.509    0.611   -.138323   .053795     .2538
>   
 *period10 | -.064848   .096764   -0.670    0.504   -.199597  -.064045   .070721
>   
 *period11 |  .000448   .105854    0.004    0.997    -.16552  -.015832   .169035
>   
 *period12 | -.031604   .097415   -0.324    0.746   -.134684  -.022647   .087102
>   
-----------+--------------------------------------------------------------------
* average dy/dx is the first difference using the min and max (i.e. usually 0 to
>  1)

.                         mat o=e(Output)

.                         replace beta = o[1,1] if n==6
(1 real change made)

.                         replace hi = o[1,1]+1.95*o[1,2]  if n==6
(1 real change made)

.                         replace lo = o[1,1]-1.95*o[1,2]  if n==6
(1 real change made)

.                 browse beta hi lo n if n<=6

.                 label define varlab 1 "t-1" 2 "t-1,t-2" 3 "t-1,t-2,t-3" 4 "Reg
> ime mean" 5 "All non-protest yrs" 6 "No lag",replace

.                 label values n varlab

.                 twoway (rspike hi lo n if n<=6,lcol(gs1)) (scatter beta n if n
> <=6,mcol(gs1)msym(O)yline(0,lcol(red)) yline(.0393769,lcol(blue)) ///
>                         ytitle("{&beta}{sub:Security personalization}",size(la
> rge))xtitle(Repression lag,height(3)) ///
>                         xscale(range(.75 6.25))yscale(range(0 .1))ylab(0(.05).
> 2) xlab(1(1)6,valuelabel angle(45))legend(off))   

.                 graph export "$dir\repression-lags.pdf", as(pdf) replace
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\repression-lags.pdf saved as PDF format

.         
.                 **** Reported Models ****
.                 xtset cowcode year

Panel variable: cowcode (unbalanced)
 Time variable: year, 1946 to 2010, but with gaps
         Delta: 1 unit

.                 gen lag4repress=l.lag3repress
(479 missing values generated)

.                 krls repress period* lag1repress lag2repress lag3repress xpers
> 2  if xonset==1, 
Iteration =  1, Looloss: 129.7565  
Iteration =  2, Looloss: 120.1496  
Iteration =  3, Looloss: 107.4626  
Iteration =  4, Looloss: 92.22689  
Iteration =  5, Looloss: 75.99965  
Iteration =  6, Looloss: 60.92178  
Iteration =  7, Looloss: 48.6774   
Iteration =  8, Looloss: 39.73535  
Iteration =  9, Looloss: 33.52335  
Iteration = 10, Looloss: 29.13533  
Iteration = 11, Looloss: 25.8469   
Iteration = 12, Looloss: 23.23919  
Iteration = 13, Looloss: 21.12421  
Iteration = 14, Looloss: 19.43496  
Iteration = 15, Looloss: 18.13537  
Iteration = 16, Looloss: 17.17613  
Iteration = 17, Looloss: 16.49401  
Iteration = 18, Looloss: 16.02473  
Iteration = 19, Looloss: 15.71122  

Pointwise Derivatives                                     Number of obs =      1
> 79 
                                                          Lambda        =   .056
> 66 
                                                          Tolerance     =     .1
> 79 
                                                          Sigma         =       
> 15 
                                                          Eff. df       =    66.
> 46 
                                                          R2            =    .97
> 06 
                                                          Looloss       =    15.
> 38

  repression |      Avg.       SE        t    P>|t|        P25       P50       P
> 75       
-------------+------------------------------------------------------------------
> --
    *period1 | -.196089   .072999   -2.686    0.008   -.728728  -.211954   .3777
> 97  
    *period2 | -.026805   .042615   -0.629    0.530   -.257213   .000078   .1944
> 75  
    *period4 | -.032411   .044619   -0.726    0.469    -.21008  -.029684    .136
> 95  
    *period5 | -.120379   .048987   -2.457    0.015   -.446115  -.040081   .2559
> 85  
    *period6 | -.004534   .047482   -0.095    0.924   -.151013  -.019311   .1381
> 57  
    *period7 | -.017578   .045909   -0.383    0.702   -.213345  -.021403   .1915
> 25  
    *period8 | -.039507   .050754   -0.778    0.437   -.163351  -.059585   .1081
> 07  
    *period9 |  .008727   .044353    0.197    0.844   -.213758   .036673   .2862
> 72  
   *period10 | -.061515   .041614   -1.478    0.141   -.232721  -.046815   .1521
> 12  
   *period11 |  -.01986   .044296   -0.448    0.654    -.16924   .035129   .1974
> 36  
   *period12 |  .003685    .04198    0.088    0.930   -.161439   .022787   .1855
> 65  
 lag1repress |  1.03706   .042292   24.521    0.000    .916061   1.03868   1.199
> 27  
 lag2repress |  .039999    .04587    0.872    0.384   -.044402   .032262   .1351
> 74  
 lag3repress | -.127115   .043586   -2.916    0.004   -.190713  -.117438  -.0590
> 48  
      xpers2 |  .023463   .011604    2.022    0.045   -.037535   .024595   .0589
> 58  
-------------+------------------------------------------------------------------
> --


.                                 est store rep1

.                 krls repress period* lag1repress lag2repress lag3repress xpers
> 2 $x1 if xonset==1, 
Iteration =  1, Looloss: 134.4992  
Iteration =  2, Looloss: 126.914   
Iteration =  3, Looloss: 116.5152  
Iteration =  4, Looloss: 103.3642  
Iteration =  5, Looloss: 88.39393  
Iteration =  6, Looloss: 73.34533  
Iteration =  7, Looloss: 60.04733  
Iteration =  8, Looloss: 49.53344  
Iteration =  9, Looloss: 41.79686  
Iteration = 10, Looloss: 36.24015  
Iteration = 11, Looloss: 32.2087   
Iteration = 12, Looloss: 29.22224  
Iteration = 13, Looloss: 26.97606  
Iteration = 14, Looloss: 25.28592  
Iteration = 15, Looloss: 24.03987  
Iteration = 16, Looloss: 23.15741  
Iteration = 17, Looloss: 22.56275  
Iteration = 18, Looloss: 22.18014  

Pointwise Derivatives                                     Number of obs =      1
> 79 
                                                          Lambda        =    .06
> 11 
                                                          Tolerance     =     .1
> 79 
                                                          Sigma         =       
> 19 
                                                          Eff. df       =    119
> .2 
                                                          R2            =     .9
> 88 
                                                          Looloss       =     21
> .8

  repression |      Avg.       SE        t    P>|t|        P25       P50       P
> 75       
-------------+------------------------------------------------------------------
> --
    *period2 | -.054353   .028654   -1.897    0.060   -.264839  -.050877   .1682
> 59  
    *period3 |   .00053   .031403    0.017    0.987   -.284362  -.011228   .2521
> 76  
    *period4 | -.010147   .031475   -0.322    0.748   -.173688  -.018672    .188
> 86  
    *period5 | -.126053   .031584   -3.991    0.000   -.459161  -.105962    .257
> 89  
    *period6 | -.031002   .030451   -1.018    0.310   -.207984  -.061947   .1382
> 42  
    *period7 | -.027382   .032458   -0.844    0.400   -.235064  -.032764   .1987
> 74  
    *period8 | -.065376   .032946   -1.984    0.049   -.275155  -.055464   .1378
> 83  
    *period9 |  .002749   .030796    0.089    0.929   -.255449   .017418   .2290
> 35  
   *period10 | -.066488   .029602   -2.246    0.026   -.253925   -.07309   .1592
> 31  
   *period11 |  .019929   .030751    0.648    0.518   -.188927   .063013    .268
> 16  
   *period12 | -.007725   .030658   -0.252    0.801   -.179936   .000499   .2096
> 81  
 lag1repress |  .708477   .019694   35.975    0.000    .618369   .723807   .8133
> 15  
 lag2repress |  .181927   .014681   12.392    0.000    .130755   .182915   .2298
> 66  
 lag3repress | -.074127    .01853   -4.000    0.000   -.145362  -.047375   .0033
> 65  
      xpers2 |  .039377   .008775    4.488    0.000   -.008026   .034551   .0905
> 35  
       lpopl |  .031612   .006919    4.569    0.000   -.003909   .029589   .0769
> 58  
    lnregion | -.017725   .006925   -2.560    0.011   -.061745  -.012324   .0263
> 61  
          lt |  -.02787   .008812   -3.163    0.002    -.07987  -.028061   .0213
> 72  
       lxyrs |  .000706   .008197    0.086    0.931   -.030865   -.00047   .0347
> 29  
-------------+------------------------------------------------------------------
> --


.                                 est store rep2

.                 krls repress period* lag1repress lag2repress lag3repress xpers
> 2 lreduration $x1  if xongoing==1, d(dx) 
Iteration =  1, Looloss: 254.4449  
Iteration =  2, Looloss: 237.1351  
Iteration =  3, Looloss: 213.7138  
Iteration =  4, Looloss: 184.6306  
Iteration =  5, Looloss: 152.2997  
Iteration =  6, Looloss: 120.7079  
Iteration =  7, Looloss: 93.66095  
Iteration =  8, Looloss: 73.00935  
Iteration =  9, Looloss: 58.40543  
Iteration = 10, Looloss: 48.36029  
Iteration = 11, Looloss: 41.3353   
Iteration = 12, Looloss: 36.23764  
Iteration = 13, Looloss: 32.43615  
Iteration = 14, Looloss: 29.59298  
Iteration = 15, Looloss: 27.50707  
Iteration = 16, Looloss: 26.02786  
Iteration = 17, Looloss: 25.0213   
Iteration = 18, Looloss: 24.36353  

Pointwise Derivatives                                     Number of obs =      3
> 28 
                                                          Lambda        =    .12
> 05 
                                                          Tolerance     =     .3
> 28 
                                                          Sigma         =       
> 20 
                                                          Eff. df       =      1
> 55 
                                                          R2            =    .98
> 03 
                                                          Looloss       =    23.
> 69

  repression |      Avg.       SE        t    P>|t|        P25       P50       P
> 75       
-------------+------------------------------------------------------------------
> --
    *period1 | -.209089   .036546   -5.721    0.000   -.790982  -.258852   .4571
> 95  
    *period2 | -.031461   .030471   -1.033    0.303   -.257801  -.021389   .2142
> 64  
    *period4 |  .024128   .030644    0.787    0.432   -.181065   .016721   .2519
> 99  
    *period5 | -.098941   .035762   -2.767    0.006   -.447645  -.126582   .3220
> 45  
    *period6 |  .038478   .030115    1.278    0.202   -.126889   .007883   .2157
> 63  
    *period7 | -.032999   .031022   -1.064    0.288   -.228437  -.019392   .2070
> 57  
    *period8 | -.046066   .028527   -1.615    0.107   -.228498  -.017548    .142
> 94  
    *period9 |  .040126   .030019    1.337    0.182   -.156911   .059219   .2335
> 65  
   *period10 | -.066968   .028299   -2.366    0.019   -.326766  -.069001   .2288
> 39  
   *period11 |  .050311   .028326    1.776    0.077   -.162882   .074897   .3009
> 66  
   *period12 | -.036521   .029045   -1.257    0.210   -.272492   -.04113   .2202
> 84  
 lag1repress |  .744997   .018849   39.524    0.000    .618716   .754163    .876
> 42  
 lag2repress |  .132566   .016177    8.195    0.000    .079922   .135444     .18
> 19  
 lag3repress | -.040304   .018871   -2.136    0.033   -.099446  -.036279    .020
> 05  
      xpers2 |  .028472   .008677    3.281    0.001   -.018576    .02391   .0711
> 83  
 lreduration | -.018044   .013397   -1.347    0.179   -.100647  -.014491   .0652
> 55  
       lpopl |  .018099   .006478    2.794    0.006    -.02207   .025106    .065
> 83  
    lnregion | -.019712   .006417   -3.072    0.002   -.061225  -.014013   .0214
> 67  
          lt | -.022757   .008499   -2.678    0.008   -.067314  -.019667   .0301
> 23  
       lxyrs |  .009823   .006522    1.506    0.133    -.01843   .011474   .0452
> 99  
-------------+------------------------------------------------------------------
> --


.                                 est store rep3

.                 krls repress period* lag1repress lag2repress lag3repress lag4r
> epress  xpers2 $x1 if xonset==1, 
Iteration =  1, Looloss: 126.3337  
Iteration =  2, Looloss: 118.5952  
Iteration =  3, Looloss: 108.2875  
Iteration =  4, Looloss: 95.73406  
Iteration =  5, Looloss: 82.08339  
Iteration =  6, Looloss: 69.02388  
Iteration =  7, Looloss: 57.97202  
Iteration =  8, Looloss: 49.40648  
Iteration =  9, Looloss: 42.95264  
Iteration = 10, Looloss: 37.96651  
Iteration = 11, Looloss: 33.98189  
Iteration = 12, Looloss: 30.77955  
Iteration = 13, Looloss: 28.26112  
Iteration = 14, Looloss: 26.34702  
Iteration = 15, Looloss: 24.94775  
Iteration = 16, Looloss: 23.96582  
Iteration = 17, Looloss: 23.30315  
Iteration = 18, Looloss: 22.87033  
Iteration = 19, Looloss: 22.59434  

Pointwise Derivatives                                     Number of obs =      1
> 71 
                                                          Lambda        =   .056
> 53 
                                                          Tolerance     =     .1
> 71 
                                                          Sigma         =       
> 19 
                                                          Eff. df       =    117
> .8 
                                                          R2            =    .99
> 02 
                                                          Looloss       =    22.
> 31

  repression |      Avg.       SE        t    P>|t|        P25       P50       P
> 75       
-------------+------------------------------------------------------------------
> --
    *period2 | -.039819   .025764   -1.546    0.124   -.203691  -.015502   .1612
> 29  
    *period3 | -.010275   .032159   -0.320    0.750   -.310561  -.041126   .2808
> 35  
    *period4 |  .011176   .026333    0.424    0.672   -.125454   .000855   .1657
> 68  
    *period6 |  -.00148    .02637   -0.056    0.955    -.15034  -.032686   .1480
> 76  
    *period7 | -.020974   .029257   -0.717    0.475   -.225064  -.006961   .1620
> 35  
    *period8 | -.018563   .025859   -0.718    0.474   -.173549  -.012415   .1286
> 55  
    *period9 |  .035846   .028784    1.245    0.215   -.190533   .050666   .2316
> 45  
   *period10 | -.041357   .024162   -1.712    0.089    -.18833  -.047309    .124
> 55  
   *period11 |  .034347   .026079    1.317    0.190    -.17836   .078864   .2487
> 07  
   *period12 |  .018344   .026092    0.703    0.483   -.128714   .014227   .2022
> 94  
 lag1repress |  .672444   .018574   36.205    0.000     .56892   .683492   .7699
> 35  
 lag2repress |  .212841   .014462   14.717    0.000    .167295   .228055   .2635
> 83  
 lag3repress |  .033117   .013732    2.412    0.017   -.010641   .035135   .0899
> 39  
 lag4repress | -.120365   .017016   -7.074    0.000   -.205306  -.087353   -.033
> 71  
      xpers2 |  .055163   .008168    6.754    0.000    .015886   .052111    .099
> 27  
       lpopl |  .038674   .006362    6.078    0.000   -.005753   .037958   .0844
> 65  
    lnregion | -.028676   .006448   -4.448    0.000   -.074501  -.021927   .0165
> 26  
          lt | -.032318   .008307   -3.891    0.000   -.091166  -.033768   .0114
> 16  
       lxyrs | -.001276   .007482   -0.171    0.865   -.036248  -.004158   .0363
> 37  
-------------+------------------------------------------------------------------
> --


.                 estout rep1 rep2 rep3 using Table1.tex, cells(b(star  fmt(%9.4
> f)) se(par fmt(%9.3f))) ///
>                         stats(r2 N N_clust) style(tex) replace label starlevel
> s(* 0.05) title(\label{tab1})
(file Table1.tex not found)
(output written to Table1.tex)

.                 twoway (hist lreduration,col(gs14)yscale(range(0 20)axis(2))ya
> xis(2)bin(30)ylab(0(0)0,axis(2)) ytitle("",axis(2))) ///
>                         (lpolyci dx_xpers2 lreduration,bw(.75) lcol(blue*1.2)l
> pat(solid)col(blue*.25)legend(off) ///
>                         xtitle("Protest campaign duration (years, log scale)",
>  ///
>                         size(small)) ytitle(Marginal effect of security person
> alism) yline(0,lcol(red))yscale(alt)yscale(alt axis(2)) ///
>                         xlab(0 "1" .69 "2"  1.099 "3"   1.61 "5" 2.079 "8" 2.4
> 8 "12"))   

.                 drop dx_* beta n hi lo

.                 graph export "$dir\pers-repression.pdf", as(pdf) replace
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\pers-repression.pdf saved as PDF format

.                 
.                 * Check with OLS RE *
.                 xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                 reg repress lag1repress lag2repress lag3repress period* xpers2
>  $x1 if xonset==1,cluster(gwf_caseid)  /* 6.3 percent marginal */
note: period1 omitted because of collinearity.

Linear regression                               Number of obs     =        179
                                                F(18, 109)        =          .
                                                Prob > F          =          .
                                                R-squared         =     0.9655
                                                Root MSE          =     .16463

                           (Std. err. adjusted for 110 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
  repression | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
 lag1repress |   1.699297   .1261631    13.47   0.000     1.449246    1.949348
 lag2repress |  -.7475196   .2236318    -3.34   0.001    -1.190751   -.3042886
 lag3repress |   .0452481   .1299852     0.35   0.728    -.2123783    .3028745
     period1 |          0  (omitted)
     period2 |   .0210623   .0294886     0.71   0.477    -.0373833    .0795078
     period3 |   .0757354   .0534934     1.42   0.160    -.0302867    .1817576
     period4 |   .0215279   .0213439     1.01   0.315     -.020775    .0638308
     period5 |  -.0280324    .036554    -0.77   0.445    -.1004811    .0444164
     period6 |   .0403839    .043736     0.92   0.358    -.0462995    .1270672
     period7 |   .0357361   .0377234     0.95   0.346    -.0390305    .1105027
     period8 |   .0210767   .0417337     0.51   0.615     -.061638    .1037915
     period9 |   .1158692   .0614994     1.88   0.062    -.0060206    .2377589
    period10 |   -.023098   .0515094    -0.45   0.655    -.1251879    .0789918
    period11 |     .04629   .0455559     1.02   0.312    -.0440003    .1365803
    period12 |   .0711832    .040809     1.74   0.084    -.0096989    .1520653
      xpers2 |   .0267899   .0138264     1.94   0.055    -.0006136    .0541934
       lpopl |   .0209891   .0115965     1.81   0.073    -.0019947     .043973
    lnregion |  -.0087238   .0146713    -0.59   0.553    -.0378018    .0203542
          lt |  -.0093529   .0157782    -0.59   0.555    -.0406249     .021919
       lxyrs |  -.0152761   .0132769    -1.15   0.252    -.0415906    .0110383
       _cons |  -.6620948   .1064808    -6.22   0.000    -.8731363   -.4510534
------------------------------------------------------------------------------

.                 xtreg repress lag1repress lag2repress lag3repress period* xper
> s2 $x1 if xonset==1,cluster(gwf_caseid)  /* 5.5 percent marginal */
note: period12 omitted because of collinearity.

Random-effects GLS regression                   Number of obs     =        179
Group variable: gwf_caseid                      Number of groups  =        110

R-squared:                                      Obs per group:
     Within  = 0.8706                                         min =          1
     Between = 0.9769                                         avg =        1.6
     Overall = 0.9655                                         max =          6

                                                Wald chi2(19)     =   14279.39
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                           (Std. err. adjusted for 110 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
  repression | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 lag1repress |   1.699297   .1261631    13.47   0.000     1.452022    1.946572
 lag2repress |  -.7475196   .2236318    -3.34   0.001     -1.18583   -.3092092
 lag3repress |   .0452481   .1299852     0.35   0.728    -.2095182    .3000144
     period1 |  -.0711832    .040809    -1.74   0.081    -.1511674     .008801
     period2 |   -.050121   .0442978    -1.13   0.258    -.1369431    .0367012
     period3 |   .0045522   .0672055     0.07   0.946    -.1271682    .1362726
     period4 |  -.0496553   .0366036    -1.36   0.175     -.121397    .0220863
     period5 |  -.0992156    .044204    -2.24   0.025    -.1858539   -.0125773
     period6 |  -.0307993   .0538162    -0.57   0.567    -.1362771    .0746784
     period7 |  -.0354472    .049858    -0.71   0.477     -.133167    .0622726
     period8 |  -.0501065   .0437846    -1.14   0.252    -.1359226    .0357097
     period9 |    .044686    .057415     0.78   0.436    -.0678454    .1572174
    period10 |  -.0942812    .061132    -1.54   0.123    -.2140978    .0255353
    period11 |  -.0248932   .0558972    -0.45   0.656    -.1344496    .0846632
    period12 |          0  (omitted)
      xpers2 |   .0267899   .0138264     1.94   0.053    -.0003093    .0538892
       lpopl |   .0209891   .0115965     1.81   0.070    -.0017395    .0437178
    lnregion |  -.0087238   .0146713    -0.59   0.552     -.037479    .0200314
          lt |  -.0093529   .0157782    -0.59   0.553    -.0402777    .0215719
       lxyrs |  -.0152761   .0132769    -1.15   0.250    -.0412985    .0107462
       _cons |  -.5909116   .1142818    -5.17   0.000    -.8148998   -.3669234
-------------+----------------------------------------------------------------
     sigma_u |          0
     sigma_e |  .18494033
         rho |          0   (fraction of variance due to u_i)
------------------------------------------------------------------------------

.                 krls repress lag1repress lag2repress lag3repress period* xpers
> 2 $x1 if xonset==1,   /* 4.8 percent marginal */
Iteration =  1, Looloss: 134.4992  
Iteration =  2, Looloss: 126.914   
Iteration =  3, Looloss: 116.5152  
Iteration =  4, Looloss: 103.3642  
Iteration =  5, Looloss: 88.39393  
Iteration =  6, Looloss: 73.34533  
Iteration =  7, Looloss: 60.04733  
Iteration =  8, Looloss: 49.53344  
Iteration =  9, Looloss: 41.79686  
Iteration = 10, Looloss: 36.24015  
Iteration = 11, Looloss: 32.2087   
Iteration = 12, Looloss: 29.22224  
Iteration = 13, Looloss: 26.97606  
Iteration = 14, Looloss: 25.28592  
Iteration = 15, Looloss: 24.03987  
Iteration = 16, Looloss: 23.15741  
Iteration = 17, Looloss: 22.56275  
Iteration = 18, Looloss: 22.18014  

Pointwise Derivatives                                     Number of obs =      1
> 79 
                                                          Lambda        =    .06
> 11 
                                                          Tolerance     =     .1
> 79 
                                                          Sigma         =       
> 19 
                                                          Eff. df       =    119
> .2 
                                                          R2            =     .9
> 88 
                                                          Looloss       =     21
> .8

  repression |      Avg.       SE        t    P>|t|        P25       P50       P
> 75       
-------------+------------------------------------------------------------------
> --
 lag1repress |  .708477   .019694   35.975    0.000    .618369   .723807   .8133
> 15  
 lag2repress |  .181927   .014681   12.392    0.000    .130755   .182915   .2298
> 66  
 lag3repress | -.074127    .01853   -4.000    0.000   -.145362  -.047375   .0033
> 65  
    *period2 | -.054353   .028654   -1.897    0.060   -.264839  -.050877   .1682
> 59  
    *period3 |   .00053   .031403    0.017    0.987   -.284362  -.011228   .2521
> 76  
    *period4 | -.010147   .031475   -0.322    0.748   -.173688  -.018672    .188
> 86  
    *period5 | -.126053   .031584   -3.991    0.000   -.459161  -.105962    .257
> 89  
    *period6 | -.031002   .030451   -1.018    0.310   -.207984  -.061947   .1382
> 42  
    *period7 | -.027382   .032458   -0.844    0.400   -.235064  -.032764   .1987
> 74  
    *period8 | -.065376   .032946   -1.984    0.049   -.275155  -.055464   .1378
> 83  
    *period9 |  .002749   .030796    0.089    0.929   -.255449   .017418   .2290
> 35  
   *period10 | -.066488   .029602   -2.246    0.026   -.253925   -.07309   .1592
> 31  
   *period11 |  .019929   .030751    0.648    0.518   -.188927   .063013    .268
> 16  
   *period12 | -.007725   .030658   -0.252    0.801   -.179936   .000499   .2096
> 81  
      xpers2 |  .039377   .008775    4.488    0.000   -.008026   .034551   .0905
> 35  
       lpopl |  .031612   .006919    4.569    0.000   -.003909   .029589   .0769
> 58  
    lnregion | -.017725   .006925   -2.560    0.011   -.061745  -.012324   .0263
> 61  
          lt |  -.02787   .008812   -3.163    0.002    -.07987  -.028061   .0213
> 72  
       lxyrs |  .000706   .008197    0.086    0.931   -.030865   -.00047   .0347
> 29  
-------------+------------------------------------------------------------------
> --


.                 
.                 * Additional covariates *
.                 gen beta = .
(4,559 missing values generated)

.                 gen n =_n

.                 gen var =""
(4,559 missing values generated)

.                 gen hi = .
(4,559 missing values generated)

.                 gen lo=.
(4,559 missing values generated)

.                 global vn = 25

.                 recode debruin_ha_cbcount (9 8 7 6 5 4 3 =3)
(49 changes made to debruin_ha_cbcount)

.                 local c="lnmembers territorial lag_xongoing civwar election co
> up coupA coupS coup12 excluded ethfrac milethnic_homo milethnic_het logoil log
> gdp xpers1 support nmc_logmilex nmc_logmilper  debruin_counterbalancing debrui
> n_cbcount debruin_ha_cbcount effective v2juhcind v2x_jucon"

.                 local i = 1

.                 foreach v of local c {
  2.                         di "`v'"
  3.                         qui xi:krls repress xpers2 period* lag1repress lag2
> repress lag3repress  $x1 `v' if xonset==1
  4.                         mat o=e(Output)
  5.                         replace beta = o[1,1] if n==`i'
  6.                         replace var = "`v'" if n==`i'
  7.                         replace hi = o[1,1]+1.95*o[1,2]  if n==`i'
  8.                         replace lo = o[1,1]-1.95*o[1,2]  if n==`i'
  9.                         local i  = `i'+1
 10.                 }
lnmembers
(1 real change made)
variable var was str1 now str9
(1 real change made)
(1 real change made)
(1 real change made)
territorial
(1 real change made)
variable var was str9 now str11
(1 real change made)
(1 real change made)
(1 real change made)
lag_xongoing
(1 real change made)
variable var was str11 now str12
(1 real change made)
(1 real change made)
(1 real change made)
civwar
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
election
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
coup
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
coupA
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
coupS
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
coup12
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
excluded
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
ethfrac
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
milethnic_homo
(1 real change made)
variable var was str12 now str14
(1 real change made)
(1 real change made)
(1 real change made)
milethnic_het
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
logoil
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
loggdp
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
xpers1
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
support
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
nmc_logmilex
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
nmc_logmilper
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
debruin_counterbalancing
(1 real change made)
variable var was str14 now str24
(1 real change made)
(1 real change made)
(1 real change made)
debruin_cbcount
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
debruin_ha_cbcount
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
effective
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
v2juhcind
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
v2x_jucon
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)

.                 label define varlab 1 "Protest size" 2 `""Territorial" "campai
> gn""' 3 `""Ongoing NV" "campaign""' 4 "Civil war" 5 "Election" ///
>                         6 "Coup attempt" 7 "Failed coup" 8 "Successful coup" 9
>  "Lag coup counts" 10 "Excluded pop." 11 "Ethnic fraction."   ///
>                         12 `""Ethnically" "homo military""' 13 `""Ethnically" 
> "hetero military""' 14 "Oil per capita (log)"  15 "GDP per capita (log)"  ///
>                         16 "Party personalism" 17 "Support party" 18 "Mil. spe
> nding" 19 "Mil. personnel" 20  "Counterbalancing"  ///
>                         21  `""Counterbalance" "count""' 22  `""Counterbalance
> " "Heavily armed""' 23 `""Effective #" "mil. orgs""' ///
>                         24  "Judicial indep." 25  "Judicial constraint",replac
> e

.                 label values n varlab

.                 twoway (rspike hi lo n if n<=$vn) (scatter beta n if n<=$vn,ms
> ym(O)mcol(gs1)yline(0,lcol(red)) yline(.0287,lcol(blue)) ///
>                         ytitle("{&beta}{sub:Security personalization}",size(la
> rge))xtitle(Added variable,height(-6)) ///
>                         xscale(range(.75 $vn.25))yscale(range(0 .08))ylab(0(.0
> 2).08) xlab(1(1)$vn,valuelabel angle(90))legend(off)xsize(8)ysize(4))         
>     

.                 graph export "$dir\added-variables-repression.pdf", as(pdf) re
> place
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\added-variables-repression.pdf saved as PDF format

.                 qui: reg repress lag_repress period* xpers2 $x1 if xonset==1,c
> luster(gwf_caseid)

.                 lincom xpers2

 ( 1)  xpers2 = 0

------------------------------------------------------------------------------
  repression | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0633317   .0256312     2.47   0.015     .0125367    .1141266
------------------------------------------------------------------------------

.                 qui: xtreg repress lag_repress period* xpers2 $x1 if xonset==1
> ,cluster(gwf_caseid)

.                 lincom xpers2

 ( 1)  xpers2 = 0

------------------------------------------------------------------------------
  repression | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0562739   .0265994     2.12   0.034       .00414    .1084078
------------------------------------------------------------------------------

. 
.                   * Check with VDem data on repression *
.                   spearman vkill repress 

 Number of obs =    4465
Spearman's rho =       0.4498

Test of H0: vkill and repression are independent
    Prob > |t| =       0.0000

.                   spearman vkill repress if xonset==1

 Number of obs =     181
Spearman's rho =       0.4037

Test of H0: vkill and repression are independent
    Prob > |t| =       0.0000

.                 krls vkill period* lag_vkill xpers2 if xonset==1 
Iteration =  1, Looloss: 123.2094  
Iteration =  2, Looloss: 117.7213  
Iteration =  3, Looloss: 110.1737  
Iteration =  4, Looloss: 100.5528  
Iteration =  5, Looloss: 89.42941  
Iteration =  6, Looloss: 77.95181  
Iteration =  7, Looloss: 67.44057  
Iteration =  8, Looloss: 58.85392  
Iteration =  9, Looloss: 52.54215  
Iteration = 10, Looloss: 48.34299  
Iteration = 11, Looloss: 45.80867  
Iteration = 12, Looloss: 44.42307  
Iteration = 13, Looloss: 43.75415  

Pointwise Derivatives                                   Number of obs =      182
>  
                                                        Lambda        =    .2457
>  
                                                        Tolerance     =     .182
>  
                                                        Sigma         =       14
>  
                                                        Eff. df       =    37.78
>  
                                                        R2            =    .8319
>  
                                                        Looloss       =    43.52

     vkill |      Avg.       SE        t    P>|t|        P25       P50       P75
>        
-----------+--------------------------------------------------------------------
  *period1 |  .051879   .118663    0.437    0.663   -.426421   .089579   .521107
>   
  *period2 |    .0828   .077799    1.064    0.289   -.181592   .132442    .37845
>   
  *period3 |  .081715   .085379    0.957    0.340     -.2523   .134905   .432384
>   
  *period4 |  .088207   .076992    1.146    0.254   -.058457    .12873   .278539
>   
  *period5 | -.107341   .081791   -1.312    0.191   -.431277  -.072191   .217954
>   
  *period6 |  .072929   .078891    0.924    0.357   -.118189   .102607   .275104
>   
  *period7 |  .016146   .080571    0.200    0.841   -.158957     .0234   .195608
>   
  *period8 | -.046834   .086858   -0.539    0.590   -.195281  -.007402   .103991
>   
  *period9 |  -.03461   .073611   -0.470    0.639   -.281187   .008449   .213957
>   
 *period10 | -.020686   .075788   -0.273    0.785     -.2522   .029912   .241172
>   
 *period11 |  .030772   .075541    0.407    0.684   -.178841    .07114   .251661
>   
 *period12 |  .072192   .077713    0.929    0.354   -.113803   .105163   .273639
>   
 lag_vkill |  .768937   .033579   22.900    0.000    .610232   .795929   .954556
>   
    xpers2 |  .042302   .021747    1.945    0.053   -.020856   .032313   .076797
>   
-----------+--------------------------------------------------------------------


.                                 est store rep1a

.                 krls vkill period* lag_vkill xpers2 $x1 if xonset==1
Iteration =  1, Looloss: 126.2738  
Iteration =  2, Looloss: 122.1085  
Iteration =  3, Looloss: 116.0872  
Iteration =  4, Looloss: 107.8871  
Iteration =  5, Looloss: 97.59026  
Iteration =  6, Looloss: 85.91093  
Iteration =  7, Looloss: 74.12179  
Iteration =  8, Looloss: 63.59553  
Iteration =  9, Looloss: 55.26136  
Iteration = 10, Looloss: 49.37397  
Iteration = 11, Looloss: 45.67385  
Iteration = 12, Looloss: 43.69954  
Iteration = 13, Looloss: 43.19043  
Iteration = 14, Looloss: 43.17692  

Pointwise Derivatives                                   Number of obs =      182
>  
                                                        Lambda        =    .3286
>  
                                                        Tolerance     =     .182
>  
                                                        Sigma         =       18
>  
                                                        Eff. df       =    70.13
>  
                                                        R2            =    .9053
>  
                                                        Looloss       =    42.98

     vkill |      Avg.       SE        t    P>|t|        P25       P50       P75
>        
-----------+--------------------------------------------------------------------
  *period1 |  .117518   .080844    1.454    0.148   -.343663   .122378   .598185
>   
  *period2 |  .076694   .053885    1.423    0.157   -.199393   .097041   .351147
>   
  *period3 |  .085953   .064112    1.341    0.182   -.204342   .110786   .399137
>   
  *period4 |  .108433   .047151    2.300    0.023   -.057468   .127815   .277271
>   
  *period5 | -.172013   .061759   -2.785    0.006   -.514902  -.148077   .185998
>   
  *period6 |  .089511   .049513    1.808    0.072   -.066222   .105416   .262929
>   
  *period7 |   .03538   .055383    0.639    0.524   -.147253   .040441   .190146
>   
  *period8 | -.096242    .03832   -2.512    0.013   -.209821  -.084866    .02434
>   
  *period9 | -.030378    .05451   -0.557    0.578   -.228422  -.007981   .148884
>   
 *period10 |  -.01418   .045314   -0.313    0.755     -.2213  -.020857   .221376
>   
 *period11 |  .008347   .051276    0.163    0.871   -.174859   .017044   .204962
>   
 *period12 |  .012389   .046937    0.264    0.792   -.131877   .027461   .171369
>   
 lag_vkill |   .68033   .022913   29.692    0.000    .527266   .706621    .86321
>   
    xpers2 |   .03362    .01673    2.010    0.046   -.012865   .038373   .074544
>   
     lpopl |  .034096   .010709    3.184    0.002    .001977   .036799   .062953
>   
  lnregion |  .005866   .013687    0.429    0.669   -.022894   .019207   .049738
>   
        lt |  .018188   .016736    1.087    0.279   -.043475   .028476   .077408
>   
     lxyrs | -.004082   .014937   -0.273    0.785   -.059326  -.013017   .039007
>   
-----------+--------------------------------------------------------------------


.                                 est store rep2a

.                 krls vkill period* lag_vkill xpers2 lreduration $x1 if xongoin
> g==1,d(dx)
Iteration =  1, Looloss: 234.0978  
Iteration =  2, Looloss: 225.2866  
Iteration =  3, Looloss: 212.6769  
Iteration =  4, Looloss: 195.7245  
Iteration =  5, Looloss: 174.7741  
Iteration =  6, Looloss: 151.4544  
Iteration =  7, Looloss: 128.3962  
Iteration =  8, Looloss: 108.2221  
Iteration =  9, Looloss: 92.53353  
Iteration = 10, Looloss: 81.60669  
Iteration = 11, Looloss: 74.75966  
Iteration = 12, Looloss: 70.93092  
Iteration = 13, Looloss: 69.10501  

Pointwise Derivatives                                     Number of obs =      3
> 32 
                                                          Lambda        =    .47
> 12 
                                                          Tolerance     =     .3
> 32 
                                                          Sigma         =       
> 19 
                                                          Eff. df       =    92.
> 69 
                                                          R2            =    .87
> 13 
                                                          Looloss       =    68.
> 49

       vkill |      Avg.       SE        t    P>|t|        P25       P50       P
> 75       
-------------+------------------------------------------------------------------
> --
    *period1 |  .148813   .067281    2.212    0.028   -.310071   .084389   .6270
> 13  
    *period2 |  .030662   .052058    0.589    0.556   -.250808   .027014   .3272
> 81  
    *period3 |  .107874   .062529    1.725    0.085   -.226093   .083885   .4616
> 05  
    *period4 |  .118336   .049922    2.370    0.018   -.113364   .115244   .3539
> 46  
    *period5 | -.073347    .06257   -1.172    0.242     -.4154  -.083442   .2624
> 13  
    *period6 |  .119147   .048402    2.462    0.014   -.061122   .107153   .2922
> 21  
    *period7 |  .040293   .047486    0.849    0.397   -.103362   .034628   .1738
> 62  
    *period8 | -.077421   .037341   -2.073    0.039   -.195087  -.083646   .0608
> 56  
    *period9 | -.022855   .046028   -0.497    0.620   -.183606  -.021139   .1323
> 14  
   *period10 | -.035926    .04628   -0.776    0.438   -.263923  -.045533   .2177
> 82  
   *period11 | -.030876    .04699   -0.657    0.512   -.228076   -.01554   .1744
> 63  
   *period12 | -.030325   .044605   -0.680    0.497   -.175407  -.024644   .1352
> 02  
   lag_vkill |  .658871   .019652   33.526    0.000    .555464   .685542   .7782
> 21  
      xpers2 |  .033396   .014812    2.255    0.025   -.004753    .03704   .0728
> 87  
 lreduration |  .010848   .021118    0.514    0.608   -.054717   .017018   .0769
> 78  
       lpopl |  .032213   .009892    3.256    0.001    .001818   .037589   .0692
> 36  
    lnregion |  -.00311   .011692   -0.266    0.790   -.041698  -.001012   .0432
> 94  
          lt |  .018065   .014635    1.234    0.218   -.024747   .018833   .0713
> 59  
       lxyrs |  .017159     .0113    1.518    0.130   -.026987   .014164   .0561
> 09  
-------------+------------------------------------------------------------------
> --


.                                 est store rep3a

.                 twoway lpolyci dx_xpers2 lreduration,bw(.7) legend(off) xtitle
> ("Protest campaign duration (log)", ///
>                         size(small)) ytitle(Marginal effect of security person
> alism) yline(0,lcol(red))

.                 drop dx_*

.                 estout rep1a rep2a rep3a using TableC2.tex, cells(b(star  fmt(
> %9.4f)) se(par fmt(%9.3f))) ///
>                 stats(r2 N N_clust) style(tex) replace label starlevels(* 0.05
> ) title(\label{tabC2})
(file TableC2.tex not found)
(output written to TableC2.tex)

.                 
.                 * Check with additive *
.                 xtset cow year

Panel variable: cowcode (unbalanced)
 Time variable: year, 1946 to 2010, but with gaps
         Delta: 1 unit

.                 gen ladd= l.additive
(2,552 missing values generated)

.                 krls additive period* ladd xpers2 $x1 if xonset==1
Iteration =  1, Looloss: 210.4518  
Iteration =  2, Looloss: 205.4408  
Iteration =  3, Looloss: 198.3815  
Iteration =  4, Looloss: 189.1355  
Iteration =  5, Looloss: 178.1774  
Iteration =  6, Looloss: 166.7539  
Iteration =  7, Looloss: 156.5542  
Iteration =  8, Looloss: 148.9698  
Iteration =  9, Looloss: 144.5435  
Iteration = 10, Looloss: 143.9177  
Iteration = 11, Looloss: 143.3769  

Pointwise Derivatives                                  Number of obs =      114 
                                                       Lambda        =    .9114 
                                                       Tolerance     =     .114 
                                                       Sigma         =       11 
                                                       Eff. df       =    33.56 
                                                       R2            =    .6828 
                                                       Looloss       =      143

 additive |      Avg.       SE        t    P>|t|        P25       P50       P75 
>       
----------+--------------------------------------------------------------------
 *period7 |  .304203   .251884    1.208    0.230   -.052994   .286967   .696316 
>  
 *period8 |  .122831   .218484    0.562    0.575   -.138499   .091564   .389052 
>  
*period10 |  .003812   .233439    0.016    0.987   -.233499  -.010399   .224222 
>  
*period11 | -.277864   .241895   -1.149    0.253   -.690515  -.333415    .03941 
>  
*period12 | -.360496   .235219   -1.533    0.128   -.606351  -.415493  -.116531 
>  
     ladd |  .342365   .041263    8.297    0.000    .229845   .371273   .452631 
>  
   xpers2 | -.292366    .07568   -3.863    0.000   -.406118  -.274758  -.128878 
>  
    lpopl | -.255429   .050137   -5.095    0.000   -.367861  -.277798  -.149667 
>  
 lnregion | -.047869   .057548   -0.832    0.407   -.147612  -.054904   .088431 
>  
       lt |  .070028   .075992    0.922    0.359    -.04108   .067002   .182953 
>  
    lxyrs | -.059726   .068126   -0.877    0.383   -.203003  -.080624   .044592 
>  
----------+--------------------------------------------------------------------


. 
.                 **** Test Repression model with other forms of personalization
>  *****  adjust Table 1, column 2 model 
.                 krls repress period* lag1repress lag2repress lag3repress gwf_p
> ers $x1 if xonset==1
Iteration =  1, Looloss: 132.1366  
Iteration =  2, Looloss: 123.4905  
Iteration =  3, Looloss: 111.8293  
Iteration =  4, Looloss: 97.41911  
Iteration =  5, Looloss: 81.50868  
Iteration =  6, Looloss: 66.10393  
Iteration =  7, Looloss: 53.07742  
Iteration =  8, Looloss: 43.29728  
Iteration =  9, Looloss: 36.52676  
Iteration = 10, Looloss: 31.95991  
Iteration = 11, Looloss: 28.77253  
Iteration = 12, Looloss: 26.38278  
Iteration = 13, Looloss: 24.47562  
Iteration = 14, Looloss: 22.92469  
Iteration = 15, Looloss: 21.69621  
Iteration = 16, Looloss: 20.77432  
Iteration = 17, Looloss: 20.1244   
Iteration = 18, Looloss: 19.69122  
Iteration = 19, Looloss: 19.41427  

Pointwise Derivatives                                      Number of obs =      
> 179 
                                                           Lambda        =   .05
> 686 
                                                           Tolerance     =     .
> 179 
                                                           Sigma         =      
>  19 
                                                           Eff. df       =    11
> 9.1 
                                                           R2            =     .
> 989 
                                                           Looloss       =    19
> .14

   repression |      Avg.       SE        t    P>|t|        P25       P50       
> P75       
--------------+-----------------------------------------------------------------
> ---
     *period1 | -.179308   .041175   -4.355    0.000   -.689927  -.214654    .39
> 601  
     *period2 | -.037083   .027164   -1.365    0.174   -.241845  -.046292   .196
> 867  
     *period4 | -.001944   .026272   -0.074    0.941   -.143222  -.011639   .137
> 669  
     *period5 | -.111865   .029824   -3.751    0.000   -.424837  -.056823   .251
> 305  
     *period6 |  .015108   .027023    0.559    0.577   -.128859  -.010762   .178
> 209  
     *period7 | -.001703   .029796   -0.057    0.955   -.196814   .003226   .234
> 998  
     *period8 | -.036911   .028794   -1.282    0.202   -.182772  -.032618   .133
> 033  
     *period9 |  .021636   .029218    0.740    0.460   -.223979   .026428   .255
> 172  
    *period10 | -.029775   .025956   -1.147    0.253   -.241063   .008228   .190
> 713  
    *period11 |  .034745   .028285    1.228    0.221   -.166576   .067611   .276
> 453  
    *period12 |  .010381   .026953    0.385    0.701   -.171931   .007121   .190
> 641  
  lag1repress |   .76888   .019736   38.959    0.000    .656143   .762508   .914
> 155  
  lag2repress |  .133193   .015662    8.504    0.000    .086592   .127909   .183
> 265  
  lag3repress |  -.06359   .019455   -3.269    0.001   -.150148   -.03498   .016
> 277  
*gwf_personal |  .026374   .024608    1.072    0.285   -.093715  -.005426   .136
> 646  
        lpopl |  .021474    .00649    3.309    0.001   -.020567   .029665   .064
> 802  
     lnregion | -.020264   .006793   -2.983    0.003   -.080483  -.017862   .038
> 538  
           lt | -.021517   .007968   -2.701    0.008    -.08322  -.024746   .036
> 073  
        lxyrs | -.003418   .007974   -0.429    0.669   -.045188  -.006122   .038
> 073  
--------------+-----------------------------------------------------------------
> ---


.                 est store p1

.                 krls repress period* lag1repress lag2repress lag3repress xpers
>  $x1 if xonset==1
Iteration =  1, Looloss: 134.5318  
Iteration =  2, Looloss: 126.9762  
Iteration =  3, Looloss: 116.6293  
Iteration =  4, Looloss: 103.5594  
Iteration =  5, Looloss: 88.69841  
Iteration =  6, Looloss: 73.7731   
Iteration =  7, Looloss: 60.58728  
Iteration =  8, Looloss: 50.15014  
Iteration =  9, Looloss: 42.4376   
Iteration = 10, Looloss: 36.83871  
Iteration = 11, Looloss: 32.68974  
Iteration = 12, Looloss: 29.51652  
Iteration = 13, Looloss: 27.04305  
Iteration = 14, Looloss: 25.12687  
Iteration = 15, Looloss: 23.69082  
Iteration = 16, Looloss: 22.66898  
Iteration = 17, Looloss: 21.98182  
Iteration = 18, Looloss: 21.54151  
Iteration = 19, Looloss: 21.26852  

Pointwise Derivatives                                     Number of obs =      1
> 79 
                                                          Lambda        =   .056
> 86 
                                                          Tolerance     =     .1
> 79 
                                                          Sigma         =       
> 19 
                                                          Eff. df       =    121
> .7 
                                                          R2            =    .98
> 92 
                                                          Looloss       =       
> 21

  repression |      Avg.       SE        t    P>|t|        P25       P50       P
> 75       
-------------+------------------------------------------------------------------
> --
    *period2 | -.047123   .027306   -1.726    0.086   -.251554  -.024719   .2048
> 21  
    *period3 |  -.01094   .029887   -0.366    0.715   -.266518  -.024316   .2547
> 53  
    *period4 | -.007215   .029802   -0.242    0.809   -.180252  -.016839   .1938
> 72  
    *period5 | -.132843   .029816   -4.455    0.000    -.46519  -.111417   .2521
> 96  
    *period6 | -.026721   .029391   -0.909    0.365   -.193759  -.053679   .1421
> 39  
    *period7 | -.024702   .030352   -0.814    0.417   -.223113  -.022466   .1907
> 47  
    *period8 | -.063487   .031014   -2.047    0.042   -.276808  -.063213   .1361
> 27  
    *period9 | -.004625   .029304   -0.158    0.875    -.22642    .01448   .2164
> 84  
   *period10 | -.053664   .028119   -1.908    0.058    -.24678  -.075625   .1811
> 89  
   *period11 |  .012362   .028396    0.435    0.664    -.17987   .042919    .246
> 17  
   *period12 | -.009453   .029304   -0.323    0.747   -.192223  -.004652   .2127
> 23  
 lag1repress |  .734034   .018424   39.841    0.000    .635914   .752488   .8515
> 67  
 lag2repress |  .176219   .014139   12.463    0.000    .124887   .170353   .2268
> 32  
 lag3repress | -.054464   .017458   -3.120    0.002   -.138849  -.036423   .0181
> 25  
       xpers |  .043718   .008645    5.057    0.000   -.012752   .040769   .1047
> 84  
       lpopl |  .023551   .006448    3.653    0.000   -.016869    .02457   .0678
> 23  
    lnregion | -.012595   .006635   -1.898    0.059   -.067137  -.000356   .0379
> 61  
          lt | -.033552   .008671   -3.869    0.000   -.085122  -.036126   .0184
> 79  
       lxyrs | -.002605   .007811   -0.334    0.739   -.042529  -.004321   .0344
> 54  
-------------+------------------------------------------------------------------
> --


.                 est store p2

.                 krls repress period* lag1repress lag2repress lag3repress xpers
> 1 $x1 if xonset==1
Iteration =  1, Looloss: 134.5278  
Iteration =  2, Looloss: 126.9659  
Iteration =  3, Looloss: 116.6082  
Iteration =  4, Looloss: 103.5252  
Iteration =  5, Looloss: 88.65718  
Iteration =  6, Looloss: 73.7458   
Iteration =  7, Looloss: 60.60989  
Iteration =  8, Looloss: 50.26123  
Iteration =  9, Looloss: 42.65946  
Iteration = 10, Looloss: 37.16663  
Iteration = 11, Looloss: 33.09817  
Iteration = 12, Looloss: 29.97666  
Iteration = 13, Looloss: 27.53837  
Iteration = 14, Looloss: 25.65611  
Iteration = 15, Looloss: 24.2598   
Iteration = 16, Looloss: 23.28135  
Iteration = 17, Looloss: 22.6355   
Iteration = 18, Looloss: 22.23004  

Pointwise Derivatives                                     Number of obs =      1
> 79 
                                                          Lambda        =    .06
> 11 
                                                          Tolerance     =     .1
> 79 
                                                          Sigma         =       
> 19 
                                                          Eff. df       =    120
> .3 
                                                          R2            =    .98
> 76 
                                                          Looloss       =    21.
> 84

  repression |      Avg.       SE        t    P>|t|        P25       P50       P
> 75       
-------------+------------------------------------------------------------------
> --
    *period2 | -.062408   .029581   -2.110    0.036   -.261454  -.070497   .1994
> 91  
    *period3 | -.003869   .031691   -0.122    0.903    -.25717  -.032904   .2568
> 88  
    *period4 |  .006002   .030831    0.195    0.846   -.157088  -.002134   .1928
> 16  
    *period5 | -.121934   .031237   -3.904    0.000   -.440745    -.0912   .2530
> 68  
    *period6 | -.004125   .030766   -0.134    0.894   -.149958  -.042116   .1575
> 98  
    *period7 |   -.0231   .032494   -0.711    0.478    -.21889  -.024348   .2029
> 37  
    *period8 | -.065037    .03338   -1.948    0.053   -.269654  -.048948   .1431
> 82  
    *period9 | -.021074   .031063   -0.678    0.498   -.246012  -.006666   .2046
> 92  
   *period10 | -.037837   .030014   -1.261    0.209   -.234806  -.044526   .1886
> 91  
   *period11 |  .026694   .031473    0.848    0.398   -.167543   .075868   .2563
> 07  
   *period12 |  .017431   .031074    0.561    0.576   -.183723   .015416   .2627
> 56  
 lag1repress |  .758374   .019795   38.312    0.000    .636957   .779172   .8850
> 74  
 lag2repress |  .151848   .015175   10.006    0.000    .096188   .148506   .2166
> 14  
 lag3repress | -.058723   .018793   -3.125    0.002   -.153113  -.025748   .0202
> 24  
      xpers1 |  .007978   .008923    0.894    0.373   -.048835   .005248   .0613
> 46  
       lpopl |  .014885   .006745    2.207    0.029    -.02889   .019921   .0636
> 44  
    lnregion | -.013521   .006987   -1.935    0.055   -.059861  -.003613   .0351
> 86  
          lt | -.023847   .008752   -2.725    0.007   -.080803  -.025942   .0225
> 13  
       lxyrs | -.012158   .008324   -1.461    0.146   -.056126  -.012161   .0310
> 84  
-------------+------------------------------------------------------------------
> --


.                 est store p3

.                 krls repress period* lag1repress lag2repress lag3repress xpers
> 2 $x1 if xonset==1
Iteration =  1, Looloss: 134.4992  
Iteration =  2, Looloss: 126.914   
Iteration =  3, Looloss: 116.5152  
Iteration =  4, Looloss: 103.3642  
Iteration =  5, Looloss: 88.39393  
Iteration =  6, Looloss: 73.34533  
Iteration =  7, Looloss: 60.04733  
Iteration =  8, Looloss: 49.53344  
Iteration =  9, Looloss: 41.79686  
Iteration = 10, Looloss: 36.24015  
Iteration = 11, Looloss: 32.2087   
Iteration = 12, Looloss: 29.22224  
Iteration = 13, Looloss: 26.97606  
Iteration = 14, Looloss: 25.28592  
Iteration = 15, Looloss: 24.03987  
Iteration = 16, Looloss: 23.15741  
Iteration = 17, Looloss: 22.56275  
Iteration = 18, Looloss: 22.18014  

Pointwise Derivatives                                     Number of obs =      1
> 79 
                                                          Lambda        =    .06
> 11 
                                                          Tolerance     =     .1
> 79 
                                                          Sigma         =       
> 19 
                                                          Eff. df       =    119
> .2 
                                                          R2            =     .9
> 88 
                                                          Looloss       =     21
> .8

  repression |      Avg.       SE        t    P>|t|        P25       P50       P
> 75       
-------------+------------------------------------------------------------------
> --
    *period2 | -.054353   .028654   -1.897    0.060   -.264839  -.050877   .1682
> 59  
    *period3 |   .00053   .031403    0.017    0.987   -.284362  -.011228   .2521
> 76  
    *period4 | -.010147   .031475   -0.322    0.748   -.173688  -.018672    .188
> 86  
    *period5 | -.126053   .031584   -3.991    0.000   -.459161  -.105962    .257
> 89  
    *period6 | -.031002   .030451   -1.018    0.310   -.207984  -.061947   .1382
> 42  
    *period7 | -.027382   .032458   -0.844    0.400   -.235064  -.032764   .1987
> 74  
    *period8 | -.065376   .032946   -1.984    0.049   -.275155  -.055464   .1378
> 83  
    *period9 |  .002749   .030796    0.089    0.929   -.255449   .017418   .2290
> 35  
   *period10 | -.066488   .029602   -2.246    0.026   -.253925   -.07309   .1592
> 31  
   *period11 |  .019929   .030751    0.648    0.518   -.188927   .063013    .268
> 16  
   *period12 | -.007725   .030658   -0.252    0.801   -.179936   .000499   .2096
> 81  
 lag1repress |  .708477   .019694   35.975    0.000    .618369   .723807   .8133
> 15  
 lag2repress |  .181927   .014681   12.392    0.000    .130755   .182915   .2298
> 66  
 lag3repress | -.074127    .01853   -4.000    0.000   -.145362  -.047375   .0033
> 65  
      xpers2 |  .039377   .008775    4.488    0.000   -.008026   .034551   .0905
> 35  
       lpopl |  .031612   .006919    4.569    0.000   -.003909   .029589   .0769
> 58  
    lnregion | -.017725   .006925   -2.560    0.011   -.061745  -.012324   .0263
> 61  
          lt |  -.02787   .008812   -3.163    0.002    -.07987  -.028061   .0213
> 72  
       lxyrs |  .000706   .008197    0.086    0.931   -.030865   -.00047   .0347
> 29  
-------------+------------------------------------------------------------------
> --


.                 est store p4

.                 krls repress period* lag1repress lag2repress lag3repress xpers
> 1 xpers2 $x1 if xonset==1
Iteration =  1, Looloss: 133.136   
Iteration =  2, Looloss: 124.8937  
Iteration =  3, Looloss: 113.6464  
Iteration =  4, Looloss: 99.51772  
Iteration =  5, Looloss: 83.59215  
Iteration =  6, Looloss: 67.81859  
Iteration =  7, Looloss: 54.19756  
Iteration =  8, Looloss: 43.81739  
Iteration =  9, Looloss: 36.59981  
Iteration = 10, Looloss: 31.79449  
Iteration = 11, Looloss: 28.57945  
Iteration = 12, Looloss: 26.35679  
Iteration = 13, Looloss: 24.77603  
Iteration = 14, Looloss: 23.64987  
Iteration = 15, Looloss: 22.86955  
Iteration = 16, Looloss: 22.3544   
Iteration = 17, Looloss: 22.03297  

Pointwise Derivatives                                     Number of obs =      1
> 79 
                                                          Lambda        =   .067
> 96 
                                                          Tolerance     =     .1
> 79 
                                                          Sigma         =       
> 20 
                                                          Eff. df       =    124
> .1 
                                                          R2            =    .98
> 86 
                                                          Looloss       =    21.
> 73

  repression |      Avg.       SE        t    P>|t|        P25       P50       P
> 75       
-------------+------------------------------------------------------------------
> --
    *period1 | -.178917   .042083   -4.252    0.000   -.725828   -.22347   .3754
> 32  
    *period2 | -.041133   .026701   -1.541    0.125   -.217707  -.024913   .2006
> 66  
    *period4 |  .027394     .0271    1.011    0.314   -.134567   .040302   .1981
> 79  
    *period5 | -.120804    .02997   -4.031    0.000   -.427688  -.101484   .2327
> 78  
    *period6 |  .025435   .025879    0.983    0.327   -.121101   .009253   .1620
> 65  
    *period7 |  .001257   .028997    0.043    0.965   -.185152   .010836   .2108
> 63  
    *period8 | -.036344   .026083   -1.393    0.165   -.210103  -.027799    .143
> 42  
    *period9 |  .020022    .02777    0.721    0.472   -.226388   .023753   .2482
> 05  
   *period10 | -.027146   .025158   -1.079    0.282   -.213729  -.040328   .1780
> 12  
   *period11 |   .05665   .028051    2.020    0.045   -.134143   .099345   .2594
> 19  
   *period12 |  .019046    .02618    0.727    0.468   -.145636   .038947   .2175
> 88  
 lag1repress |  .660454   .016919   39.037    0.000     .58204   .684323   .7630
> 65  
 lag2repress |  .191076   .011843   16.134    0.000    .146676   .187303   .2409
> 25  
 lag3repress | -.043807   .015969   -2.743    0.007   -.116717  -.022682   .0330
> 66  
      xpers1 |  -.00792   .008375   -0.946    0.346   -.048138  -.012759   .0298
> 87  
      xpers2 |  .053025   .008355    6.346    0.000    .001885   .045453   .1076
> 62  
       lpopl |  .030293   .006249    4.847    0.000   -.004109   .031436   .0731
> 38  
    lnregion | -.012453   .006589   -1.890    0.061   -.056144  -.007132   .0322
> 03  
          lt | -.034198   .008513   -4.017    0.000   -.077511  -.037362   .0114
> 47  
       lxyrs |  .000268   .007689    0.035    0.972   -.036569   .005225   .0406
> 61  
-------------+------------------------------------------------------------------
> --


.                 est store p5

.                 estout p1 p2 p3 p4 p5  using TableC3.tex, cells(b(star  fmt(%9
> .4f)) se(par fmt(%9.3f))) ///
>                 stats(r2 N N_clust) style(tex) replace label starlevels(* 0.05
> ) title(\label{tabC3})
(file TableC3.tex not found)
(output written to TableC3.tex)

.                                         
.                 *** Repression ECMs ***
.                 use temp-fe,clear

.                 gen ongXpers=xongoing*xpers2

.                 gen onsXpers = xonset*xpers2

.                 gen nvtime =lreduration
(4,226 missing values generated)

.                 recode nvtime (.=0)
(4226 changes made to nvtime)

.                 local var  = "repress xongoing ongXpers xpers2 civwar election
>  coup coupS coupA lnregion lpop"

.                 foreach v of local var {
  2.                         xtset gwf_caseid year
  3.                         qui gen d`v'=d.`v'
  4.                         qui gen l`v'=l.`v'
  5.                 }

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                 
.                 * ECMs *
.                 global x= "dcivwar lcivwar delection lelection dcoupS lcoupS d
> coupA lcoupA"

.                 reghdfe drepress lrepress dxongoing lxongoing dxpers2 lxpers2 
> lt lxyrs, ///
>                         absorb(period* nvtime) vce(cluster gwf_caseid nvtime)
(warning: absorbing 13 dimensions of fixed effects; check that you really want t
> hat)
(dropped 1 singleton observations)
(MWFE estimator converged in 7 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gel
> bach & Miller applied.
warning: missing F statistic; dropped variables due to collinearity or too few c
> lusters

HDFE Linear regression                            Number of obs   =      4,193
Absorbing 13 HDFE groups                          F(   7,     10) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0272
                                                  Adj R-squared   =     0.0202
Number of clusters (gwf_caseid) =        252      Within R-sq.    =     0.0098
Number of clusters (nvtime)  =         11         Root MSE        =     0.1447

                     (Std. err. adjusted for 11 clusters in gwf_caseid nvtime)
------------------------------------------------------------------------------
             |               Robust
    drepress | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    lrepress |  -.0123193   .0009517   -12.94   0.000    -.0144398   -.0101988
   dxongoing |   .0379831    .001896    20.03   0.000     .0337585    .0422077
   lxongoing |   .0402292   .0070867     5.68   0.000      .024439    .0560193
     dxpers2 |   .0150026   .0017216     8.71   0.000     .0111667    .0188386
     lxpers2 |   .0037301   .0015586     2.39   0.038     .0002573    .0072028
          lt |  -.0004339   .0011223    -0.39   0.707    -.0029346    .0020668
       lxyrs |   .0028803   .0017878     1.61   0.138    -.0011032    .0068637
       _cons |  -.0048266   .0006261    -7.71   0.000    -.0062215   -.0034316
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     period1 |         2           0           2     |
     period2 |         2           1           1     |
     period3 |         2           1           1    ?|
     period4 |         2           1           1    ?|
     period5 |         2           1           1    ?|
     period6 |         2           1           1    ?|
     period7 |         2           1           1    ?|
     period8 |         2           1           1    ?|
     period9 |         2           1           1    ?|
    period10 |         2           1           1    ?|
    period11 |         2           1           1    ?|
    period12 |         2           1           1    ?|
      nvtime |        11          11           0    *|
-----------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation

.                 centile xpers2 if e(sample)==1,centile(50)

                                                          Binom. interp.   
    Variable |       Obs  Percentile    Centile        [95% conf. interval]
-------------+-------------------------------------------------------------
      xpers2 |     4,193         50    .0329928       -.1364154    .1599759

.                 est store r1

.                 reghdfe drepress lrepress dxongoing lxongoing dxpers2 lxpers2 
> lt lxyrs $x, ///
>                         absorb(period* nvtime) vce(cluster gwf_caseid nvtime)
(warning: absorbing 13 dimensions of fixed effects; check that you really want t
> hat)
(dropped 1 singleton observations)
(MWFE estimator converged in 5 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gel
> bach & Miller applied.
warning: missing F statistic; dropped variables due to collinearity or too few c
> lusters

HDFE Linear regression                            Number of obs   =      4,158
Absorbing 13 HDFE groups                          F(  15,     10) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0394
                                                  Adj R-squared   =     0.0306
Number of clusters (gwf_caseid) =        251      Within R-sq.    =     0.0222
Number of clusters (nvtime)  =         11         Root MSE        =     0.1444

                     (Std. err. adjusted for 11 clusters in gwf_caseid nvtime)
------------------------------------------------------------------------------
             |               Robust
    drepress | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    lrepress |  -.0126958    .001086   -11.69   0.000    -.0151156    -.010276
   dxongoing |   .0304887    .002521    12.09   0.000     .0248716    .0361059
   lxongoing |   .0318921   .0066742     4.78   0.001      .017021    .0467631
     dxpers2 |   .0140631   .0022737     6.19   0.000      .008997    .0191292
     lxpers2 |   .0028809   .0016103     1.79   0.104    -.0007071    .0064689
          lt |   .0012612   .0012313     1.02   0.330    -.0014824    .0040047
       lxyrs |   .0023436   .0016682     1.40   0.190    -.0013734    .0060605
     dcivwar |    .026353   .0064371     4.09   0.002     .0120103    .0406957
     lcivwar |  -.0015073   .0047728    -0.32   0.759    -.0121418    .0091273
   delection |  -.0140142   .0068975    -2.03   0.070    -.0293828    .0013544
   lelection |  -.0260923   .0049803    -5.24   0.000     -.037189   -.0149956
      dcoupS |    .075963   .0078224     9.71   0.000     .0585336    .0933923
      lcoupS |    .100857   .0195129     5.17   0.000     .0573796    .1443344
      dcoupA |   .0359067   .0069222     5.19   0.000     .0204831    .0513303
      lcoupA |   .0283996   .0078649     3.61   0.005     .0108756    .0459236
       _cons |  -.0061048   .0013507    -4.52   0.001    -.0091144   -.0030952
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     period1 |         2           0           2     |
     period2 |         2           1           1     |
     period3 |         2           1           1    ?|
     period4 |         2           1           1    ?|
     period5 |         2           1           1    ?|
     period6 |         2           1           1    ?|
     period7 |         2           1           1    ?|
     period8 |         2           1           1    ?|
     period9 |         2           1           1    ?|
    period10 |         2           1           1    ?|
    period11 |         2           1           1    ?|
    period12 |         2           1           1    ?|
      nvtime |        11          11           0    *|
-----------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation

.                 est store r2

.                 reghdfe drepress lrepress dxongoing dongXpers lxongoing longXp
> ers dxpers2 lxpers2 lt lxyrs, ///
>                         absorb(period* nvtime) vce(cluster gwf_caseid nvtime)
(warning: absorbing 13 dimensions of fixed effects; check that you really want t
> hat)
(dropped 1 singleton observations)
(MWFE estimator converged in 7 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gel
> bach & Miller applied.
warning: missing F statistic; dropped variables due to collinearity or too few c
> lusters

HDFE Linear regression                            Number of obs   =      4,193
Absorbing 13 HDFE groups                          F(   9,     10) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0290
                                                  Adj R-squared   =     0.0215
Number of clusters (gwf_caseid) =        252      Within R-sq.    =     0.0116
Number of clusters (nvtime)  =         11         Root MSE        =     0.1446

                     (Std. err. adjusted for 11 clusters in gwf_caseid nvtime)
------------------------------------------------------------------------------
             |               Robust
    drepress | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    lrepress |  -.0124417   .0008702   -14.30   0.000    -.0143805   -.0105028
   dxongoing |   .0405113   .0016538    24.50   0.000     .0368264    .0441962
   dongXpers |   .0143142    .007931     1.80   0.101    -.0033572    .0319856
   lxongoing |   .0352675   .0046547     7.58   0.000     .0248962    .0456388
   longXpers |  -.0147324   .0178323    -0.83   0.428    -.0544652    .0250004
     dxpers2 |   .0115939   .0015694     7.39   0.000     .0080971    .0150907
     lxpers2 |   .0045457   .0005762     7.89   0.000     .0032619    .0058296
          lt |  -.0002375   .0010468    -0.23   0.825      -.00257     .002095
       lxyrs |   .0022896   .0011169     2.05   0.068     -.000199    .0047782
       _cons |  -.0046299   .0006427    -7.20   0.000     -.006062   -.0031979
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     period1 |         2           0           2     |
     period2 |         2           1           1     |
     period3 |         2           1           1    ?|
     period4 |         2           1           1    ?|
     period5 |         2           1           1    ?|
     period6 |         2           1           1    ?|
     period7 |         2           1           1    ?|
     period8 |         2           1           1    ?|
     period9 |         2           1           1    ?|
    period10 |         2           1           1    ?|
    period11 |         2           1           1    ?|
    period12 |         2           1           1    ?|
      nvtime |        11          11           0    *|
-----------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation

.                 est store r3

.                 reghdfe drepress lrepress dxongoing dongXpers lxongoing longXp
> ers dxpers2 lxpers2 lt lxyrs $x, ///
>                         absorb(period* nvtime) vce(cluster gwf_caseid nvtime)
(warning: absorbing 13 dimensions of fixed effects; check that you really want t
> hat)
(dropped 1 singleton observations)
(MWFE estimator converged in 5 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gel
> bach & Miller applied.
warning: missing F statistic; dropped variables due to collinearity or too few c
> lusters

HDFE Linear regression                            Number of obs   =      4,158
Absorbing 13 HDFE groups                          F(  17,     10) =          .
Statistics robust to heteroskedasticity           Prob > F        =          .
                                                  R-squared       =     0.0415
                                                  Adj R-squared   =     0.0322
Number of clusters (gwf_caseid) =        251      Within R-sq.    =     0.0243
Number of clusters (nvtime)  =         11         Root MSE        =     0.1443

                     (Std. err. adjusted for 11 clusters in gwf_caseid nvtime)
------------------------------------------------------------------------------
             |               Robust
    drepress | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    lrepress |  -.0128206    .001053   -12.17   0.000    -.0151669   -.0104743
   dxongoing |   .0329244    .002208    14.91   0.000     .0280046    .0378442
   dongXpers |   .0144249   .0075459     1.91   0.085    -.0023885    .0312383
   lxongoing |   .0261523    .005088     5.14   0.000     .0148154    .0374891
   longXpers |  -.0164042   .0174619    -0.94   0.370    -.0553118    .0225033
     dxpers2 |   .0104789   .0019154     5.47   0.000     .0062111    .0147468
     lxpers2 |   .0037987   .0005815     6.53   0.000      .002503    .0050943
          lt |   .0015499   .0012922     1.20   0.258    -.0013293    .0044291
       lxyrs |   .0016912    .001041     1.62   0.135    -.0006282    .0040106
     dcivwar |   .0265803   .0065569     4.05   0.002     .0119707      .04119
     lcivwar |  -.0018368   .0051065    -0.36   0.727    -.0132148    .0095411
   delection |  -.0135343   .0064897    -2.09   0.064    -.0279942    .0009256
   lelection |   -.024904   .0048908    -5.09   0.000    -.0358013   -.0140066
      dcoupS |   .0766511    .008176     9.38   0.000     .0584338    .0948685
      lcoupS |   .1020861   .0188062     5.43   0.000     .0601834    .1439889
      dcoupA |   .0368695   .0077811     4.74   0.001     .0195322    .0542068
      lcoupA |   .0294337   .0082616     3.56   0.005     .0110258    .0478416
       _cons |  -.0059952   .0013179    -4.55   0.001    -.0089316   -.0030587
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     period1 |         2           0           2     |
     period2 |         2           1           1     |
     period3 |         2           1           1    ?|
     period4 |         2           1           1    ?|
     period5 |         2           1           1    ?|
     period6 |         2           1           1    ?|
     period7 |         2           1           1    ?|
     period8 |         2           1           1    ?|
     period9 |         2           1           1    ?|
    period10 |         2           1           1    ?|
    period11 |         2           1           1    ?|
    period12 |         2           1           1    ?|
      nvtime |        11          11           0    *|
-----------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation

.                 est store r4

.                 
.                                 * Check ECM split sample *
.                                 qui reghdfe drepress lrepress dxongoing lxongo
> ing  lt lxyrs if xpers2>.0260124,  absorb(period* nvtime) vce(cluster gwf_case
> id)

.                                 lincom dxongoing

 ( 1)  dxongoing = 0

------------------------------------------------------------------------------
    drepress | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0548435   .0260829     2.10   0.037     .0033299     .106357
------------------------------------------------------------------------------

.                                 qui reghdfe drepress lrepress dxongoing lxongo
> ing  lt lxyrs if xpers2< .0260124,  absorb(period* nvtime) vce(cluster gwf_cas
> eid)

.                                 lincom dxongoing

 ( 1)  dxongoing = 0

------------------------------------------------------------------------------
    drepress | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0237919    .017331     1.37   0.172    -.0104212     .058005
------------------------------------------------------------------------------

.                                 qui reghdfe drepress lrepress dxongoing lxongo
> ing  lt lxyrs $x if xpers2> .0260124,  absorb(period* nvtime) vce(cluster gwf_
> caseid)

.                                 lincom dxongoing

 ( 1)  dxongoing = 0

------------------------------------------------------------------------------
    drepress | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    .047443   .0254513     1.86   0.064    -.0028233    .0977092
------------------------------------------------------------------------------

.                                 qui reghdfe drepress lrepress dxongoing lxongo
> ing  lt lxyrs $x if xpers2<.0260124,  absorb(period* nvtime) vce(cluster gwf_c
> aseid)

.                                 lincom dxongoing

 ( 1)  dxongoing = 0

------------------------------------------------------------------------------
    drepress | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0173761   .0176144     0.99   0.325    -.0174009    .0521532
------------------------------------------------------------------------------

.                                 
.                  * ECM plots *
.                         interflex drepress dxongoing xpers2 lrepress lxongoing
>  lnregion lpop lt lxyrs, ///
>                                 fe(period* nvtime) cluster(gwf_caseid) type(ke
> rnel)  bw(5)

.                         mat e=r(margeff)

.                         gen g=_n

.                         gen b=.
(4,559 missing values generated)

.                         gen se=.
(4,559 missing values generated)

.                         gen npers=.
(4,559 missing values generated)

.                         forval i = 1(1)50 {
  2.                                 qui replace npers=e[`i',1] if g==`i'
  3.                                 qui replace b=e[`i',2] if g==`i'
  4.                                 qui replace se=e[`i',3] if g==`i'
  5.                         }

.                         gen hi5=.
(4,559 missing values generated)

.                         gen hi10=.
(4,559 missing values generated)

.                         gen lo5=.
(4,559 missing values generated)

.                         gen lo10=.
(4,559 missing values generated)

.                         replace hi5  = b+1.96*se
(50 real changes made)

.                         replace hi10 = b+1.65*se
(50 real changes made)

.                         replace lo5  = b-1.96*se
(50 real changes made)

.                         replace lo10 = b-1.65*se
(50 real changes made)

.                         graph twoway (hist xpers2,bin(50)freq bcolor(gs14)ysca
> le(off)) ///
>                                 (rspike hi5 lo5 npers if g<=50,lcolor(blue*.35
> )lwidth(thin)yaxis(2)yscale(alt axis(2)) ///
>                                 xtitle("Security personalization",size(small)h
> eight(4)) ///
>                                 yline(0,lp(dash)axis(2)) ytitle("Marginal effe
> ct of Protest campaign on Repression", ///
>                                 size(small) height(4)axis(2))ylab(-.12(.04).12
> ,axis(2)) ///
>                                 title("Short-run",size(med))) ///
>                                 (rspike hi10 lo10 npers if g<=50,lcolor(blue*.
> 35)lwidth(medthick)yaxis(2)yscale(alt axis(2))) ///
>                                 (line b npers if g<=50,yaxis(2)yscale(alt axis
> (2))lpattern(solid)lcolor(blue*1.1) ///
>                                 xscale(range(0 1)) xlab(-1.6(.8)1.6)legend(lab
> (1 "Security personalization") ///
>                                 lab(2 "95% CI") lab(4 "Marginal effect") size(
> small)pos(6)col(3)ring(1)) ///
>                                 legend(order(1 2 4))saving(g1.gph,replace)) 
(note:  named style med not found in class gsize, default attributes used)
(file g1.gph not found)
file g1.gph saved

.                         drop se b g npers 

.                         interflex drepress lxongoing xpers2 lrepress dxongoing
>  lnregion lpop lt lxyrs, ///
>                                 fe(period* nvtime) cluster(gwf_caseid) type(ke
> rnel)  bw(5)

.                         mat e=r(margeff)

.                         gen g=_n

.                         gen b=.
(4,559 missing values generated)

.                         gen se=.
(4,559 missing values generated)

.                         gen npers=.
(4,559 missing values generated)

.                         forval i = 1(1)50 {
  2.                                 qui replace npers=e[`i',1] if g==`i'
  3.                                 qui replace b=e[`i',2] if g==`i'
  4.                                 qui replace se=e[`i',3] if g==`i'
  5.                         }

.                         replace hi5  = b+1.96*se
(50 real changes made)

.                         replace hi10 = b+1.65*se
(50 real changes made)

.                         replace lo5  = b-1.96*se
(50 real changes made)

.                         replace lo10 = b-1.65*se
(50 real changes made)

.                         graph twoway (hist xpers2,bin(50)freq bcolor(gs14)ysca
> le(off)) ///
>                                 (rspike hi5 lo5 npers if g<=50,lcolor(blue*.35
> )lwidth(thin)yaxis(2)yscale(alt axis(2)) ///
>                                 xtitle("Security personalization",size(small)h
> eight(4)) ///
>                                 yline(0,lp(dash)axis(2)) ytitle("Marginal effe
> ct of Protest campaign on Repression", ///
>                                 size(small) height(4)axis(2))ylab(-.12(.04).12
> ,axis(2)) ///
>                                 title("Long-run",size(med))) ///
>                                 (rspike hi10 lo10 npers if g<=50,lcolor(blue*.
> 35)lwidth(medthick)yaxis(2)yscale(alt axis(2))) ///
>                                 (line b npers if g<=50,yaxis(2)yscale(alt axis
> (2))lpattern(solid)lcolor(blue*1.1) ///
>                                 xscale(range(0 1)) xlab(-1.6(.8)1.6)legend(lab
> (1 "Security personalization") ///
>                                 lab(2 "95% CI") lab(4 "Marginal effect") size(
> small)pos(6)col(3)ring(1)) ///
>                                 legend(order(1 2 4))saving(g2.gph,replace)) 
(note:  named style med not found in class gsize, default attributes used)
(file g2.gph not found)
file g2.gph saved

.                 gr combine g1.gph g2.gph,xsize(8) 
(note:  named style med not found in class gsize, default attributes used)
(note:  named style med not found in class gsize, default attributes used)

.                 graph export "$dir\interflex-repression.pdf", as(pdf) replace 
>  
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\interflex-repression.pdf saved as PDF format

.                 drop se b g npers 

. 
.                 *** Onset year relative to post-onset years ***
.                 xi:krls repress xpers2 xonset civwar lpop lnmembers lt if xong
> oing==1,d(dx) 
Iteration =  1, Looloss: 248.6122  
Iteration =  2, Looloss: 240.2559  
Iteration =  3, Looloss: 229.8712  
Iteration =  4, Looloss: 218.1209  
Iteration =  5, Looloss: 206.183   
Iteration =  6, Looloss: 195.321   
Iteration =  7, Looloss: 186.3853  
Iteration =  8, Looloss: 179.6693  
Iteration =  9, Looloss: 175.0957  
Iteration = 10, Looloss: 172.4305  
Iteration = 11, Looloss: 171.6716  
Iteration = 12, Looloss: 171.6672  

Pointwise Derivatives                                   Number of obs =      311
>  
                                                        Lambda        =    1.293
>  
                                                        Tolerance     =     .311
>  
                                                        Sigma         =        6
>  
                                                        Eff. df       =    34.87
>  
                                                        R2            =    .4902
>  
                                                        Looloss       =    171.4

repression |      Avg.       SE        t    P>|t|        P25       P50       P75
>        
-----------+--------------------------------------------------------------------
    xpers2 |  .032611   .036006    0.906    0.366   -.117218    .02195   .181162
>   
   *xonset | -.114714   .094418   -1.215    0.225   -.261572  -.109758   .066179
>   
   *civwar |  .680164   .158423    4.293    0.000     .26334   .671426   1.14661
>   
     lpopl |  .172445   .025552    6.749    0.000     .07351   .178531   .274989
>   
 lnmembers |  .088845   .015927    5.578    0.000   -.015249   .090225   .193236
>   
        lt | -.065623   .033174   -1.978    0.049   -.231326  -.045641   .109204
>   
-----------+--------------------------------------------------------------------


.                 twoway lpolyci dx_xonset xpers2,legend(off) yline(0,lcol(red))

.                 drop dx*

.                         
.                 ******* NAVCO repression **********
.                 use temp-fe,clear

.                 gen hipers = xpers2>0 if xpers2~=.

.                 tab any_repression if xonset==1

 = 1 if any |
      State |
 Violence / |
 Repression |
         of |
Campaign, 0 |
       o.w. |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         21       11.93       11.93
          1 |        155       88.07      100.00
------------+-----------------------------------
      Total |        176      100.00

. 
.                  * Onset years *
.                 tab any_repression hipers if xonset==1,col

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

= 1 if any |
     State |
Violence / |
Repression |
        of |
 Campaign, |        hipers
    0 o.w. |         0          1 |     Total
-----------+----------------------+----------
         0 |        16          5 |        21 
           |     15.69       6.76 |     11.93 
-----------+----------------------+----------
         1 |        86         69 |       155 
           |     84.31      93.24 |     88.07 
-----------+----------------------+----------
     Total |       102         74 |       176 
           |    100.00     100.00 |    100.00 

.                 krls any_repression xpers2 if xonset==1  

Pointwise Derivatives                                       Number of obs =     
>  176 
                                                            Lambda        =    4
> 7.04 
                                                            Tolerance     =     
> .176 
                                                            Sigma         =     
>    1 
                                                            Eff. df       =    1
> .823 
                                                            R2            =   .0
> 2602 
                                                            Looloss       =    5
> 6.55

any_repression |      Avg.       SE        t    P>|t|        P25       P50      
>  P75       
---------------+----------------------------------------------------------------
> ----
        xpers2 |  .021549   .008309    2.593    0.010    .012239     .0257   .02
> 9014  
---------------+----------------------------------------------------------------
> ----


.                 krls any_repression xpers2 lnregion lpop lnmembers lt period* 
> if xonset==1,d(k1)  
Iteration =  1, Looloss: 53.531    
Iteration =  2, Looloss: 53.30028  
Iteration =  3, Looloss: 52.96314  
Iteration =  4, Looloss: 52.50567  
Iteration =  5, Looloss: 51.94764  
Iteration =  6, Looloss: 51.35912  
Iteration =  7, Looloss: 50.85547  

Pointwise Derivatives                                       Number of obs =     
>  173 
                                                            Lambda        =    3
> .476 
                                                            Tolerance     =     
> .173 
                                                            Sigma         =     
>   17 
                                                            Eff. df       =    2
> 0.01 
                                                            R2            =    .
> 2516 
                                                            Looloss       =    5
> 0.57

any_repression |      Avg.       SE        t    P>|t|        P25       P50      
>  P75       
---------------+----------------------------------------------------------------
> ----
        xpers2 |  .035344   .009234    3.828    0.000    .021117   .033757   .04
> 6078  
      lnregion | -.022472   .007924   -2.836    0.005   -.033108  -.017933  -.00
> 7953  
         lpopl |  .019498   .006428    3.033    0.003    .012181   .019913   .02
> 5669  
     lnmembers |  .006875   .004701    1.462    0.146    .002264   .007856   .01
> 1665  
            lt | -.005073   .009079   -0.559    0.577   -.016819  -.005201   .00
> 4145  
      *period1 |  .018066   .041841    0.432    0.667   -.047012  -.005336   .05
> 4688  
      *period2 |  .038021   .043748    0.869    0.386   -.008339   .023903   .07
> 0709  
      *period3 | -.005886   .043487   -0.135    0.893   -.055936  -.023934   .02
> 2683  
      *period4 |  .024977   .036347    0.687    0.493   -.012438   .014775   .05
> 2435  
      *period5 |  .032067   .046745    0.686    0.494   -.029211   .006501    .0
> 6624  
      *period6 |  .008968   .038827    0.231    0.818   -.026409   .000353   .03
> 1806  
      *period7 | -.003928   .040366   -0.097    0.923   -.045391  -.014956   .02
> 1338  
      *period8 | -.044156   .025912   -1.704    0.090    -.05514  -.039156  -.02
> 6984  
      *period9 | -.015568    .03919   -0.397    0.692   -.047961  -.024659   .01
> 0542  
     *period10 |  .028276   .036097    0.783    0.435   -.006888   .018652   .04
> 9501  
     *period11 | -.000535   .038337   -0.014    0.989   -.033421  -.010547   .02
> 0673  
     *period12 |  .027286   .035256    0.774    0.440   -.004734   .018774   .04
> 3717  
---------------+----------------------------------------------------------------
> ----
* average dy/dx is the first difference using the min and max (i.e. usually 0 to
>  1)

.                 krls any_repression xpers2 lnregion lpop lnmembers lt period* 
> lag1repress lag2repress lag3repress if xonset==1
Iteration =  1, Looloss: 53.29208  
Iteration =  2, Looloss: 53.12086  
Iteration =  3, Looloss: 52.87122  
Iteration =  4, Looloss: 52.54085  
Iteration =  5, Looloss: 52.16177  
Iteration =  6, Looloss: 51.80788  

Pointwise Derivatives                                       Number of obs =     
>  171 
                                                            Lambda        =    3
> .455 
                                                            Tolerance     =     
> .171 
                                                            Sigma         =     
>   19 
                                                            Eff. df       =     
> 21.9 
                                                            R2            =    .
> 2606 
                                                            Looloss       =    5
> 1.55

any_repression |      Avg.       SE        t    P>|t|        P25       P50      
>  P75       
---------------+----------------------------------------------------------------
> ----
        xpers2 |  .029263   .008344    3.507    0.001    .018066    .02847   .03
> 8688  
      lnregion | -.018458   .007267   -2.540    0.012   -.025129  -.016098  -.00
> 7613  
         lpopl |  .012875   .005726    2.249    0.026    .007243   .012249   .01
> 7423  
     lnmembers |  .003647   .004253    0.857    0.393    .000696   .004238   .00
> 7759  
            lt |   -.0045   .008362   -0.538    0.591   -.013352  -.004796   .00
> 2332  
      *period2 |  .032866   .042442    0.774    0.440   -.010944   .020086   .06
> 4733  
      *period3 | -.013253   .040238   -0.329    0.742   -.053505  -.030246   .00
> 8307  
      *period4 |  .027403   .035392    0.774    0.440   -.004534   .016936   .05
> 5419  
      *period5 |  .027687   .045342    0.611    0.542   -.028625    .00948   .07
> 1731  
      *period6 |  -.00097   .037146   -0.026    0.979   -.025423  -.012129   .01
> 4569  
      *period7 | -.015865   .038301   -0.414    0.679   -.049938   -.02524    .0
> 0313  
      *period8 | -.045254   .024861   -1.820    0.071   -.059326  -.043177  -.02
> 7861  
      *period9 | -.013748    .03742   -0.367    0.714   -.052818   -.02199   .02
> 1453  
     *period10 |  .021741   .035119    0.619    0.537   -.016817   .010932   .05
> 3354  
     *period11 | -.001728   .036946   -0.047    0.963   -.030351   -.01216    .0
> 1649  
     *period12 |  .030574   .034136    0.896    0.372    .003325   .022289   .05
> 2699  
   lag1repress |  .026056   .006486    4.018    0.000    .013027    .02794   .03
> 8887  
   lag2repress |  .008326   .005802    1.435    0.153   -.002667   .010076   .01
> 7918  
   lag3repress | -.002038   .006499   -0.314    0.754   -.010576  -.001618   .00
> 6666  
---------------+----------------------------------------------------------------
> ----


.                 twoway lpolyci k1_xpers2 year,bw(8) lcol(blue*1.2)lpat(solid)c
> ol(blue*.25)legend(off) ///
>                         xtitle("Year", size(small)) saving(h1.gph,replace) ///
>                         ytitle(Marginal effect of security personalism)ylab(0 
> .02 .04 .06) ///
>                         yline(0,lcol(red))  xlab(1950(10)2010)tit(Onset years 
> only)
(file h1.gph not found)
file h1.gph saved

.                         
.                 * All campaign years *
.                 tab any_repression hipers if xongoing==1,col

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

= 1 if any |
     State |
Violence / |
Repression |
        of |
 Campaign, |        hipers
    0 o.w. |         0          1 |     Total
-----------+----------------------+----------
         0 |        20          7 |        27 
           |     10.31       5.83 |      8.60 
-----------+----------------------+----------
         1 |       174        113 |       287 
           |     89.69      94.17 |     91.40 
-----------+----------------------+----------
     Total |       194        120 |       314 
           |    100.00     100.00 |    100.00 

.                 krls any_repression xpers2 if xongoing==1  

Pointwise Derivatives                                       Number of obs =     
>  314 
                                                            Lambda        =    8
> 3.36 
                                                            Tolerance     =     
> .314 
                                                            Sigma         =     
>    1 
                                                            Eff. df       =    1
> .825 
                                                            R2            =   .0
> 1365 
                                                            Looloss       =    8
> 7.68

any_repression |      Avg.       SE        t    P>|t|        P25       P50      
>  P75       
---------------+----------------------------------------------------------------
> ----
        xpers2 |  .012963   .005866    2.210    0.028    .003453   .013248   .02
> 5886  
---------------+----------------------------------------------------------------
> ----


.                 krls any_repression xpers2 lreduration lnregion lpop lnmembers
>  lt period* if xongoing==1,d(k2)  
Iteration =  1, Looloss: 83.20963  
Iteration =  2, Looloss: 82.66353  
Iteration =  3, Looloss: 81.90018  
Iteration =  4, Looloss: 80.89268  
Iteration =  5, Looloss: 79.66224  
Iteration =  6, Looloss: 78.30011  
Iteration =  7, Looloss: 76.95673  
Iteration =  8, Looloss: 75.79614  
Iteration =  9, Looloss: 74.9474   

Pointwise Derivatives                                       Number of obs =     
>  310 
                                                            Lambda        =    1
> .562 
                                                            Tolerance     =     
>  .31 
                                                            Sigma         =     
>   18 
                                                            Eff. df       =    4
> 6.98 
                                                            R2            =    .
> 3292 
                                                            Looloss       =    7
> 4.45

any_repression |      Avg.       SE        t    P>|t|        P25       P50      
>  P75       
---------------+----------------------------------------------------------------
> ----
        xpers2 |  .049159   .009469    5.192    0.000    .021459   .040067   .06
> 8172  
   lreduration |  .033445   .013927    2.401    0.017    .017411   .029692   .04
> 6686  
      lnregion | -.020006   .007674   -2.607    0.010   -.030862  -.011437  -.00
> 1218  
         lpopl |  .018436   .006826    2.701    0.007     .00706    .01754   .02
> 8788  
     lnmembers |  .011121   .004691    2.371    0.018    .003649   .010512   .01
> 7178  
            lt | -.020955   .009117   -2.298    0.022   -.035686  -.020422  -.00
> 7257  
      *period1 |  .026265   .043529    0.603    0.547   -.049274  -.002887   .06
> 5201  
      *period2 |  .037629   .037139    1.013    0.312   -.019341    .01971    .0
> 6697  
      *period3 | -.000511   .040888   -0.012    0.990   -.055013  -.026057   .02
> 0272  
      *period4 |  .035127   .034224    1.026    0.306   -.020261   .017743   .06
> 6573  
      *period5 |  .035612   .043276    0.823    0.411   -.037353   .010256   .07
> 4298  
      *period6 |  .019805   .033146    0.598    0.551   -.021687    .00523   .05
> 0223  
      *period7 | -.003615   .033207   -0.109    0.913   -.041626  -.020645      
> .019  
      *period8 | -.065709   .022513   -2.919    0.004    -.09191  -.058723  -.03
> 6117  
      *period9 | -.001692   .030453   -0.056    0.956   -.032074  -.010585    .0
> 1559  
     *period10 |  .032414   .031908    1.016    0.311   -.014954   .015159   .05
> 8585  
     *period11 |  .004212   .031996    0.132    0.895   -.035253  -.011428   .03
> 4011  
     *period12 |  .033823   .028927    1.169    0.243   -.007307   .021461    .0
> 5598  
---------------+----------------------------------------------------------------
> ----
* average dy/dx is the first difference using the min and max (i.e. usually 0 to
>  1)

.                 krls any_repression xpers2 lreduration lnregion lpop lnmembers
>  lt period* lag1repress lag2repress lag3repress if xongoing==1
Iteration =  1, Looloss: 82.85031  
Iteration =  2, Looloss: 82.31765  
Iteration =  3, Looloss: 81.55452  
Iteration =  4, Looloss: 80.52979  
Iteration =  5, Looloss: 79.26727  
Iteration =  6, Looloss: 77.86418  
Iteration =  7, Looloss: 76.46842  
Iteration =  8, Looloss: 75.22238  
Iteration =  9, Looloss: 74.21882  
Iteration = 10, Looloss: 73.49602  
Iteration = 11, Looloss: 73.05571  
Iteration = 12, Looloss: 72.98375  

Pointwise Derivatives                                       Number of obs =     
>  307 
                                                            Lambda        =    .
> 9972 
                                                            Tolerance     =     
> .307 
                                                            Sigma         =     
>   20 
                                                            Eff. df       =    6
> 4.26 
                                                            R2            =    .
> 4412 
                                                            Looloss       =    7
> 2.89

any_repression |      Avg.       SE        t    P>|t|        P25       P50      
>  P75       
---------------+----------------------------------------------------------------
> ----
        xpers2 |  .046848   .009737    4.811    0.000    .020997   .037167   .07
> 0312  
   lreduration |  .014703   .014311    1.027    0.305    .001077   .015432   .02
> 8798  
      lnregion | -.020311   .007679   -2.645    0.009   -.033812  -.013374  -.00
> 2274  
         lpopl |  .011008   .007247    1.519    0.130    .000789   .007961   .02
> 0669  
     lnmembers |  .006613   .004777    1.384    0.167     .00058   .007567   .01
> 3701  
            lt |  -.02041   .009219   -2.214    0.028   -.037673  -.021594  -.00
> 4477  
      *period1 |  .022405   .043586    0.514    0.608   -.056096  -.021508    .0
> 5382  
      *period2 |  .028341   .038585    0.735    0.463   -.028195    .00446   .05
> 2458  
      *period3 | -.011413   .043167   -0.264    0.792   -.063494  -.036686   .00
> 4321  
      *period4 |  .031124   .036969    0.842    0.401   -.020137   .010231   .05
> 5776  
      *period6 | -.019146   .036639   -0.523    0.602   -.049811  -.027844  -.00
> 3216  
      *period7 | -.044657   .036545   -1.222    0.223   -.085707  -.048677  -.01
> 5607  
      *period8 | -.093193   .028273   -3.296    0.001   -.134682    -.0857  -.04
> 5667  
      *period9 | -.025144   .034063   -0.738    0.461   -.068917  -.035196   .00
> 9906  
     *period10 |  .012374   .034354    0.360    0.719   -.043673  -.009255   .04
> 7662  
     *period11 | -.007118    .03519   -0.202    0.840   -.052345  -.025434    .0
> 1807  
     *period12 |  .026173    .03237    0.809    0.419   -.018598   .012286   .05
> 2341  
   lag1repress |  .070525   .010769    6.549    0.000    .038418    .06699   .09
> 7537  
   lag2repress |  .009569   .007089    1.350    0.178   -.011756   .011759   .02
> 9204  
   lag3repress | -.031517   .010308   -3.057    0.002   -.048589   -.02998  -.01
> 2626  
---------------+----------------------------------------------------------------
> ----


.                 twoway lpolyci k2_xpers2 lreduration,bw(.5) lcol(blue*1.2)lpat
> (solid)col(blue*.25)legend(off) ///
>                         xtitle("Protest campaign duration (years, log scale)",
>  size(small)) saving(h2.gph,replace) ///
>                         ytitle(Marginal effect of security personalism)ylab(0 
> .02 .04 .06) tit(All campaign years) ///
>                         yline(0,lcol(red))  xlab(0 "1" .69 "2"  1.39 "4"    2.
> 079 "8" 2.48 "12")
(file h2.gph not found)
file h2.gph saved

.                 gr combine h1.gph h2.gph,xsize(8)

.                 graph export "$dir\navco-repression.pdf", as(pdf)   replace
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\navco-repression.pdf saved as PDF format

.                 
.         ******************************************************
.         ***** Security forces defection during campaign ******
.         ******************************************************
.                 use temp-fe,clear

.                 xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                 qui sum lnmembers if any_sec_def~=.

.                 gen xmembers = (lnmembers-r(mean))/ r(sd)
(4,246 missing values generated)

.                 qui sum xpers2

.                 gen ipers2 = (xpers2+abs(r(min)))/(r(max)+abs(r(min)))

.                 global x1 = "lt period2-period12 lpop xmembers lnregion repres
> s lreduration"

.                 xi:logit any_sec_defect  xpers2 period2-period12,  vce(cluster
>  id)

Iteration 0:   log pseudolikelihood = -208.27253  
Iteration 1:   log pseudolikelihood = -183.68172  
Iteration 2:   log pseudolikelihood = -183.44135  
Iteration 3:   log pseudolikelihood = -183.44068  
Iteration 4:   log pseudolikelihood = -183.44068  

Logistic regression                                     Number of obs =    316
                                                        Wald chi2(12) =  33.71
                                                        Prob > chi2   = 0.0008
Log pseudolikelihood = -183.44068                       Pseudo R2     = 0.1192

                                    (Std. err. adjusted for 182 clusters in id)
-------------------------------------------------------------------------------
              |               Robust
any_sec_def~t | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
       xpers2 |   .4926721   .1923288     2.56   0.010     .1157145    .8696297
      period2 |   1.071129    .764127     1.40   0.161    -.4265329     2.56879
      period3 |   3.059314   .7762319     3.94   0.000     1.537927      4.5807
      period4 |   .3475346   1.292709     0.27   0.788    -2.186128    2.881197
      period5 |   2.133419   1.387698     1.54   0.124     -.586419    4.853258
      period6 |   .5867275   1.218423     0.48   0.630    -1.801338    2.974793
      period7 |   .6124506     1.1926     0.51   0.608    -1.725003    2.949904
      period8 |   .0250932   1.103304     0.02   0.982    -2.137343    2.187529
      period9 |   .3186445   1.146149     0.28   0.781    -1.927766    2.565055
     period10 |   -.567972   1.166155    -0.49   0.626    -2.853594     1.71765
     period11 |   -.706379   1.300582    -0.54   0.587    -3.255472    1.842715
     period12 |  -1.184221   1.202989    -0.98   0.325    -3.542037    1.173594
        _cons |  -.6287977   1.048043    -0.60   0.549    -2.682924    1.425329
-------------------------------------------------------------------------------

.                 est store def1

.                 xi:logit any_sec_defect  xpers2 lt period2-period12,  vce(clus
> ter id)

Iteration 0:   log pseudolikelihood = -208.27253  
Iteration 1:   log pseudolikelihood = -182.20926  
Iteration 2:   log pseudolikelihood = -181.91678  
Iteration 3:   log pseudolikelihood = -181.91565  
Iteration 4:   log pseudolikelihood = -181.91565  

Logistic regression                                     Number of obs =    316
                                                        Wald chi2(13) =  36.71
                                                        Prob > chi2   = 0.0005
Log pseudolikelihood = -181.91565                       Pseudo R2     = 0.1265

                                    (Std. err. adjusted for 182 clusters in id)
-------------------------------------------------------------------------------
              |               Robust
any_sec_def~t | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
       xpers2 |   .6267442   .2220385     2.82   0.005     .1915568    1.061932
           lt |  -.2520389   .1954067    -1.29   0.197     -.635029    .1309513
      period2 |   1.184106   .7216434     1.64   0.101    -.2302891    2.598501
      period3 |      3.117   .7652879     4.07   0.000     1.617063    4.616936
      period4 |   .6377025   1.255576     0.51   0.612     -1.82318    3.098585
      period5 |   2.423348   1.324247     1.83   0.067    -.1721287    5.018825
      period6 |   .6908899    1.18084     0.59   0.558    -1.623514    3.005294
      period7 |   .7040193   1.162891     0.61   0.545    -1.575206    2.983244
      period8 |   .2344882   1.054987     0.22   0.824    -1.833249    2.302225
      period9 |    .541572   1.112111     0.49   0.626    -1.638126     2.72127
     period10 |    -.45789   1.122068    -0.41   0.683    -2.657103    1.741323
     period11 |  -.5270015   1.259695    -0.42   0.676    -2.995958    1.941955
     period12 |  -.9796815   1.162052    -0.84   0.399    -3.257262    1.297899
        _cons |  -.8023562   .9982769    -0.80   0.422    -2.758943    1.154231
-------------------------------------------------------------------------------

.                 est store def2

.                 xi:logit any_sec_defect  xpers2 $x1,  vce(cluster id)

Iteration 0:   log pseudolikelihood = -204.35784  
Iteration 1:   log pseudolikelihood = -159.64575  
Iteration 2:   log pseudolikelihood = -158.11176  
Iteration 3:   log pseudolikelihood = -158.09613  
Iteration 4:   log pseudolikelihood = -158.09612  

Logistic regression                                     Number of obs =    311
                                                        Wald chi2(18) =  74.81
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -158.09612                       Pseudo R2     = 0.2264

                                    (Std. err. adjusted for 178 clusters in id)
-------------------------------------------------------------------------------
              |               Robust
any_sec_def~t | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
       xpers2 |   .5335284   .2401067     2.22   0.026     .0629279    1.004129
           lt |  -.1887852   .2006929    -0.94   0.347    -.5821361    .2045657
      period2 |   1.387888   .9229746     1.50   0.133    -.4211085    3.196886
      period3 |   2.981817    .837637     3.56   0.000     1.340079    4.623556
      period4 |   .4456636   1.250277     0.36   0.722    -2.004834    2.896161
      period5 |   2.479951   1.393913     1.78   0.075    -.2520685    5.211971
      period6 |   .2745424   1.171707     0.23   0.815     -2.02196    2.571045
      period7 |  -.0789263   1.203285    -0.07   0.948    -2.437322     2.27947
      period8 |   .1257563   1.085577     0.12   0.908    -2.001935    2.253448
      period9 |   .4268899   1.241854     0.34   0.731    -2.007099    2.860879
     period10 |   -.218805   1.164084    -0.19   0.851    -2.500368    2.062758
     period11 |   -.256396   1.187592    -0.22   0.829    -2.584033    2.071241
     period12 |  -.9082888    1.17658    -0.77   0.440    -3.214343    1.397765
        lpopl |  -.5339994   .1823531    -2.93   0.003    -.8914049   -.1765939
     xmembers |   .7959461   .2610828     3.05   0.002     .2842333    1.307659
     lnregion |   .0136748   .1545529     0.09   0.929    -.2892433    .3165929
   repression |   .4223964   .2868347     1.47   0.141    -.1397894    .9845821
  lreduration |   -.626639   .3093922    -2.03   0.043    -1.233037   -.0202415
        _cons |   4.575364   1.873772     2.44   0.015     .9028381    8.247889
-------------------------------------------------------------------------------

.                 est store def3

.                 xi:logit any_sec_defect  xpers2 $x1 milethnic_homo,  vce(clust
> er id)

Iteration 0:   log pseudolikelihood = -204.35784  
Iteration 1:   log pseudolikelihood = -153.22153  
Iteration 2:   log pseudolikelihood =  -151.3913  
Iteration 3:   log pseudolikelihood =  -151.3729  
Iteration 4:   log pseudolikelihood =  -151.3729  

Logistic regression                                     Number of obs =    311
                                                        Wald chi2(19) =  81.42
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -151.3729                        Pseudo R2     = 0.2593

                                    (Std. err. adjusted for 178 clusters in id)
-------------------------------------------------------------------------------
              |               Robust
any_sec_def~t | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
       xpers2 |   .4750647   .2304612     2.06   0.039      .023369    .9267604
           lt |  -.1231261   .2043894    -0.60   0.547     -.523722    .2774698
      period2 |   .7354377   .9622249     0.76   0.445    -1.150488    2.621364
      period3 |    2.34386   .7907784     2.96   0.003     .7939623    3.893757
      period4 |  -.5749511   1.137964    -0.51   0.613     -2.80532    1.655418
      period5 |   1.358799   1.389385     0.98   0.328    -1.364345    4.081944
      period6 |  -1.019741   1.153716    -0.88   0.377    -3.280983    1.241502
      period7 |  -1.186835   1.139921    -1.04   0.298    -3.421039     1.04737
      period8 |  -.7564632    1.00608    -0.75   0.452    -2.728344    1.215418
      period9 |  -.4980226   1.214499    -0.41   0.682    -2.878396    1.882351
     period10 |  -1.012893   1.017549    -1.00   0.320    -3.007253    .9814676
     period11 |  -.9292224    1.28192    -0.72   0.469    -3.441739    1.583294
     period12 |  -1.883227   1.180937    -1.59   0.111    -4.197821    .4313663
        lpopl |  -.5893134   .1811275    -3.25   0.001    -.9443168   -.2343101
     xmembers |   .7822535   .2503641     3.12   0.002     .2915488    1.272958
     lnregion |  -.0286293   .1598067    -0.18   0.858    -.3418446    .2845861
   repression |   .7287026   .3089684     2.36   0.018     .1231357    1.334269
  lreduration |  -.5226107   .3033563    -1.72   0.085    -1.117178    .0719569
milethnic_~mo |  -1.653734   .6880235    -2.40   0.016    -3.002235   -.3052326
        _cons |   6.181977   1.915939     3.23   0.001     2.426805    9.937149
-------------------------------------------------------------------------------

.                 margins,dydx(xpers2 xmembers milethnic_homo) /* lnmembers & xp
> ers2 both now Stdev==1 scale for margins */

Average marginal effects                                   Number of obs = 311
Model VCE: Robust

Expression: Pr(any_sec_defect), predict()
dy/dx wrt:  xpers2 xmembers milethnic_homo

-------------------------------------------------------------------------------
              |            Delta-method
              |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
       xpers2 |   .0763834   .0358964     2.13   0.033     .0060277     .146739
     xmembers |   .1257748   .0373496     3.37   0.001      .052571    .1989785
milethnic_~mo |  -.2658959   .0998929    -2.66   0.008    -.4616823   -.0701094
-------------------------------------------------------------------------------

.                 est store def4

.                 xi:logit any_sec_defect  ipers2 $x1 milethnic_homo,  vce(clust
> er id)

Iteration 0:   log pseudolikelihood = -204.35784  
Iteration 1:   log pseudolikelihood = -153.22153  
Iteration 2:   log pseudolikelihood =  -151.3913  
Iteration 3:   log pseudolikelihood =  -151.3729  
Iteration 4:   log pseudolikelihood =  -151.3729  

Logistic regression                                     Number of obs =    311
                                                        Wald chi2(19) =  81.42
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -151.3729                        Pseudo R2     = 0.2593

                                    (Std. err. adjusted for 178 clusters in id)
-------------------------------------------------------------------------------
              |               Robust
any_sec_def~t | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
       ipers2 |    1.56861   .7609569     2.06   0.039     .0771617    3.060058
           lt |  -.1231261   .2043894    -0.60   0.547     -.523722    .2774698
      period2 |   .7354377   .9622249     0.76   0.445    -1.150488    2.621364
      period3 |    2.34386   .7907784     2.96   0.003     .7939623    3.893757
      period4 |  -.5749511   1.137964    -0.51   0.613     -2.80532    1.655418
      period5 |   1.358799   1.389385     0.98   0.328    -1.364345    4.081944
      period6 |  -1.019741   1.153716    -0.88   0.377    -3.280983    1.241502
      period7 |  -1.186835   1.139921    -1.04   0.298    -3.421039     1.04737
      period8 |  -.7564632    1.00608    -0.75   0.452    -2.728344    1.215418
      period9 |  -.4980226   1.214499    -0.41   0.682    -2.878396    1.882351
     period10 |  -1.012893   1.017549    -1.00   0.320    -3.007253    .9814675
     period11 |  -.9292225    1.28192    -0.72   0.469    -3.441739    1.583294
     period12 |  -1.883227   1.180937    -1.59   0.111    -4.197821    .4313663
        lpopl |  -.5893134   .1811275    -3.25   0.001    -.9443168     -.23431
     xmembers |   .7822535   .2503641     3.12   0.002     .2915488    1.272958
     lnregion |  -.0286293   .1598067    -0.18   0.858    -.3418446    .2845861
   repression |   .7287026   .3089684     2.36   0.018     .1231357    1.334269
  lreduration |  -.5226107   .3033564    -1.72   0.085    -1.117178    .0719569
milethnic_~mo |  -1.653734   .6880235    -2.40   0.016    -3.002235   -.3052326
        _cons |   5.444858   1.989343     2.74   0.006     1.545818    9.343898
-------------------------------------------------------------------------------

.                 sum ipers2 xmembers milethnic_homo if e(sample)==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      ipers2 |        311    .4012737    .3046336          0          1
    xmembers |        311   -.0029252    1.000744  -2.480257   2.470261
milethnic~mo |        311     .170418    .3766056          0          1

.                 margins,dydx(ipers2 xmembers milethnic_homo)  /* milethnic_hom
> o & xpers2 both now (0,1) scale for margins */

Average marginal effects                                   Number of obs = 311
Model VCE: Robust

Expression: Pr(any_sec_defect), predict()
dy/dx wrt:  ipers2 xmembers milethnic_homo

-------------------------------------------------------------------------------
              |            Delta-method
              |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
       ipers2 |   .2522092   .1185258     2.13   0.033     .0199029    .4845155
     xmembers |   .1257748   .0373496     3.37   0.001      .052571    .1989785
milethnic_~mo |  -.2658959   .0998929    -2.66   0.008    -.4616823   -.0701094
-------------------------------------------------------------------------------

.                 krls any_sec_defect xpers2 
Iteration =  1, Looloss: 150.6245  

Pointwise Derivatives                                       Number of obs =     
>  316 
                                                            Lambda        =    5
> 1.81 
                                                            Tolerance     =     
> .316 
                                                            Sigma         =     
>    1 
                                                            Eff. df       =    2
> .309 
                                                            R2            =   .0
> 2861 
                                                            Looloss       =    1
> 50.2

any_sec_defect |      Avg.       SE        t    P>|t|        P25       P50      
>  P75       
---------------+----------------------------------------------------------------
> ----
        xpers2 |  .025351   .012685    1.999    0.047   -.019728   .052598   .08
> 3629  
---------------+----------------------------------------------------------------
> ----


.                 xi:krls any_sec_defect xpers2 $x1
Iteration =  1, Looloss: 147.2811  
Iteration =  2, Looloss: 145.9941  
Iteration =  3, Looloss: 144.1006  
Iteration =  4, Looloss: 141.4253  
Iteration =  5, Looloss: 137.8483  
Iteration =  6, Looloss: 133.3921  
Iteration =  7, Looloss: 128.2857  
Iteration =  8, Looloss: 122.9501  
Iteration =  9, Looloss: 117.8887  
Iteration = 10, Looloss: 113.541   
Iteration = 11, Looloss: 110.1868  
Iteration = 12, Looloss: 107.9384  
Iteration = 13, Looloss: 106.776   
Iteration = 14, Looloss: 107.1084  

Pointwise Derivatives                                       Number of obs =     
>  311 
                                                            Lambda        =    .
> 5295 
                                                            Tolerance     =     
> .311 
                                                            Sigma         =     
>   18 
                                                            Eff. df       =    8
> 4.66 
                                                            R2            =    .
> 6218 
                                                            Looloss       =    1
> 06.6

any_sec_defect |      Avg.       SE        t    P>|t|        P25       P50      
>  P75       
---------------+----------------------------------------------------------------
> ----
        xpers2 |  .052471   .016777    3.128    0.002   -.008873   .042976   .10
> 3585  
            lt | -.005611   .015581   -0.360    0.719   -.063519  -.007251   .04
> 5265  
      *period2 |   .14145   .058377    2.423    0.016     .01344    .15382   .30
> 9133  
      *period3 |  .215677   .067153    3.212    0.001    .050486   .285254   .41
> 0235  
      *period4 |  -.02379   .056022   -0.425    0.671   -.190957  -.036505   .15
> 4793  
      *period5 |  .190129   .068534    2.774    0.006    .006741   .250757   .41
> 8693  
      *period6 |  .040168   .058924    0.682    0.496   -.042786   .064058   .14
> 2189  
      *period7 | -.014922   .058962   -0.253    0.800   -.113564   .009305   .09
> 6149  
      *period8 | -.031805    .04719   -0.674    0.501    -.11103  -.015474   .05
> 1716  
      *period9 |  .051294   .053609    0.957    0.339   -.078501   .078394   .19
> 0561  
     *period10 | -.047785     .0526   -0.908    0.364   -.171911  -.044198   .07
> 9006  
     *period11 | -.008417   .053134   -0.158    0.874   -.128786  -.000983   .10
> 3738  
     *period12 | -.084697   .050697   -1.671    0.096   -.199235  -.083116   .04
> 0369  
         lpopl | -.045117   .012243   -3.685    0.000   -.081555  -.042098  -.00
> 7396  
      xmembers |  .103268   .016437    6.282    0.000    .042289   .095813   .16
> 1762  
      lnregion |  .007309   .012836    0.569    0.569   -.013541   .013274    .0
> 3093  
    repression |  .019137   .020937    0.914    0.361   -.028601   .022245   .06
> 9513  
   lreduration | -.053138   .024108   -2.204    0.028   -.114692  -.056658   .02
> 0458  
---------------+----------------------------------------------------------------
> ----


.                 local c="lag_xongoing election civwar priordem coup coupA coup
> S excluded milethnic_het nmc_logmilper nmc_logmilex logoil loggdp xpers1 suppo
> rt"

.                 foreach v of local c {
  2.                         di "`v'"
  3.                         qui xi:logit any_sec_defect xpers2 $x1 i.region `v'
> , cluster(id)
  4.                         lincom xpers2
  5.                         qui xi:krls any_sec_defect xpers2 $x1 i.region `v'
  6.                         matrix b = e(b)
  7.                         di b[1,1]
  8.                 }
lag_xongoing

 ( 1)  [any_sec_defect]xpers2 = 0

------------------------------------------------------------------------------
any_sec_de~t | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    .533945   .2501962     2.13   0.033     .0435694     1.02432
------------------------------------------------------------------------------
.04662266
election

 ( 1)  [any_sec_defect]xpers2 = 0

------------------------------------------------------------------------------
any_sec_de~t | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .4965728   .2426248     2.05   0.041     .0210369    .9721086
------------------------------------------------------------------------------
.0402507
civwar

 ( 1)  [any_sec_defect]xpers2 = 0

------------------------------------------------------------------------------
any_sec_de~t | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .5255962   .2472607     2.13   0.034     .0409742    1.010218
------------------------------------------------------------------------------
.03444406
priordem

 ( 1)  [any_sec_defect]xpers2 = 0

------------------------------------------------------------------------------
any_sec_de~t | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .4608007   .2482361     1.86   0.063    -.0257331    .9473345
------------------------------------------------------------------------------
.03040732
coup

 ( 1)  [any_sec_defect]xpers2 = 0

------------------------------------------------------------------------------
any_sec_de~t | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .4633608   .2451979     1.89   0.059    -.0172183    .9439399
------------------------------------------------------------------------------
.0380031
coupA

 ( 1)  [any_sec_defect]xpers2 = 0

------------------------------------------------------------------------------
any_sec_de~t | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .5210173   .2496409     2.09   0.037     .0317301    1.010304
------------------------------------------------------------------------------
.0463519
coupS

 ( 1)  [any_sec_defect]xpers2 = 0

------------------------------------------------------------------------------
any_sec_de~t | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .4696556   .2428018     1.93   0.053    -.0062273    .9455384
------------------------------------------------------------------------------
.03709253
excluded

 ( 1)  [any_sec_defect]xpers2 = 0

------------------------------------------------------------------------------
any_sec_de~t | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    .502517   .2442466     2.06   0.040     .0238024    .9812316
------------------------------------------------------------------------------
.04058905
milethnic_het

 ( 1)  [any_sec_defect]xpers2 = 0

------------------------------------------------------------------------------
any_sec_de~t | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .4141574   .2418546     1.71   0.087     -.059869    .8881838
------------------------------------------------------------------------------
.02637627
nmc_logmilper

 ( 1)  [any_sec_defect]xpers2 = 0

------------------------------------------------------------------------------
any_sec_de~t | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .4312434     .24477     1.76   0.078    -.0484969    .9109838
------------------------------------------------------------------------------
.02901999
nmc_logmilex

 ( 1)  [any_sec_defect]xpers2 = 0

------------------------------------------------------------------------------
any_sec_de~t | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |      .4036   .2497837     1.62   0.106    -.0859672    .8931671
------------------------------------------------------------------------------
.02948984
logoil

 ( 1)  [any_sec_defect]xpers2 = 0

------------------------------------------------------------------------------
any_sec_de~t | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .4979354   .2427084     2.05   0.040     .0222357     .973635
------------------------------------------------------------------------------
.04517911
loggdp

 ( 1)  [any_sec_defect]xpers2 = 0

------------------------------------------------------------------------------
any_sec_de~t | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .3666531   .2421268     1.51   0.130    -.1079067    .8412129
------------------------------------------------------------------------------
.0130364
xpers1

 ( 1)  [any_sec_defect]xpers2 = 0

------------------------------------------------------------------------------
any_sec_de~t | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .4501073   .2454646     1.83   0.067    -.0309945    .9312091
------------------------------------------------------------------------------
.03457555
support

 ( 1)  [any_sec_defect]xpers2 = 0

------------------------------------------------------------------------------
any_sec_de~t | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .4858736   .2405393     2.02   0.043     .0144253     .957322
------------------------------------------------------------------------------
.04174397

.                   * modeling unit hetero increases variance but also increases
>  estimate size; so NOT modeling unit effect yields LOWER estimates, which are 
> reported *
.                 xi:logit any_sec_defect xpers2 $x1,cluster(gwf_caseid)

Iteration 0:   log pseudolikelihood = -204.35784  
Iteration 1:   log pseudolikelihood = -159.64575  
Iteration 2:   log pseudolikelihood = -158.11176  
Iteration 3:   log pseudolikelihood = -158.09613  
Iteration 4:   log pseudolikelihood = -158.09612  

Logistic regression                                     Number of obs =    311
                                                        Wald chi2(18) =  92.84
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -158.09612                       Pseudo R2     = 0.2264

                            (Std. err. adjusted for 115 clusters in gwf_caseid)
-------------------------------------------------------------------------------
              |               Robust
any_sec_def~t | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
       xpers2 |   .5335284   .2427178     2.20   0.028     .0578103    1.009247
           lt |  -.1887852   .1909885    -0.99   0.323    -.5631158    .1855454
      period2 |   1.387888   .9628898     1.44   0.149    -.4993408    3.275118
      period3 |   2.981817   .8338048     3.58   0.000      1.34759    4.616045
      period4 |   .4456636   1.270977     0.35   0.726    -2.045405    2.936732
      period5 |   2.479951    1.43802     1.72   0.085    -.3385167    5.298419
      period6 |   .2745424   1.227982     0.22   0.823    -2.132258    2.681343
      period7 |  -.0789263   1.144532    -0.07   0.945    -2.322168    2.164315
      period8 |   .1257563   1.055458     0.12   0.905    -1.942903    2.194415
      period9 |   .4268899   1.223731     0.35   0.727    -1.971579    2.825359
     period10 |   -.218805   1.176096    -0.19   0.852    -2.523911    2.086301
     period11 |   -.256396   1.225826    -0.21   0.834    -2.658971    2.146179
     period12 |  -.9082888   1.184383    -0.77   0.443    -3.229637    1.413059
        lpopl |  -.5339994   .1921911    -2.78   0.005     -.910687   -.1573118
     xmembers |   .7959461   .2484337     3.20   0.001     .3090251    1.282867
     lnregion |   .0136748   .1453591     0.09   0.925    -.2712237    .2985733
   repression |   .4223964   .3106714     1.36   0.174    -.1865083    1.031301
  lreduration |   -.626639   .3552924    -1.76   0.078    -1.322999    .0697213
        _cons |   4.575364   1.929787     2.37   0.018      .793051    8.357676
-------------------------------------------------------------------------------

.                 xi:xtlogit any_sec_defect xpers2 $x1,vce(cluster gwf_caseid)

Fitting comparison model:

Iteration 0:   log pseudolikelihood = -204.35784  
Iteration 1:   log pseudolikelihood = -159.64575  
Iteration 2:   log pseudolikelihood = -158.11176  
Iteration 3:   log pseudolikelihood = -158.09613  
Iteration 4:   log pseudolikelihood = -158.09612  

Fitting full model:

tau =  0.0     log pseudolikelihood = -158.09612
tau =  0.1     log pseudolikelihood =  -153.6258
tau =  0.2     log pseudolikelihood = -149.56752
tau =  0.3     log pseudolikelihood = -145.73876
tau =  0.4     log pseudolikelihood = -142.03991
tau =  0.5     log pseudolikelihood = -138.41191
tau =  0.6     log pseudolikelihood = -134.82848
tau =  0.7     log pseudolikelihood = -131.31991
tau =  0.8     log pseudolikelihood = -128.07453

Iteration 0:   log pseudolikelihood = -131.31353  
Iteration 1:   log pseudolikelihood = -122.44526  
Iteration 2:   log pseudolikelihood = -119.88286  
Iteration 3:   log pseudolikelihood = -118.82469  
Iteration 4:   log pseudolikelihood = -118.82256  
Iteration 5:   log pseudolikelihood = -118.82256  (backed up)
Iteration 6:   log pseudolikelihood = -118.81355  
Iteration 7:   log pseudolikelihood = -118.81355  

Calculating robust standard errors ...

Random-effects logistic regression                   Number of obs    =    311
Group variable: gwf_caseid                           Number of groups =    115

Random effects u_i ~ Gaussian                        Obs per group:
                                                                  min =      1
                                                                  avg =    2.7
                                                                  max =     21

Integration method: mvaghermite                      Integration pts. =     12

                                                     Wald chi2(18)    =  21.17
Log pseudolikelihood = -118.81355                    Prob > chi2      = 0.2712

                            (Std. err. adjusted for 115 clusters in gwf_caseid)
-------------------------------------------------------------------------------
              |               Robust
any_sec_def~t | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
       xpers2 |   .7585435   .6851886     1.11   0.268    -.5844014    2.101488
           lt |   .2539914   .5264086     0.48   0.629    -.7777505    1.285733
      period2 |  -.7375899   2.975386    -0.25   0.804     -6.56924     5.09406
      period3 |   3.297696   1.516002     2.18   0.030     .3263861    6.269005
      period4 |  -6.728562   3.766294    -1.79   0.074    -14.11036    .6532387
      period5 |  -.8217431    3.84109    -0.21   0.831     -8.35014    6.706654
      period6 |  -3.766672   3.350698    -1.12   0.261    -10.33392    2.800575
      period7 |  -5.082951   3.711823    -1.37   0.171    -12.35799    2.192089
      period8 |  -4.700285   3.026935    -1.55   0.120    -10.63297    1.232398
      period9 |  -5.888627   3.613735    -1.63   0.103    -12.97142    1.194162
     period10 |  -5.746156   3.137694    -1.83   0.067    -11.89592    .4036111
     period11 |  -6.205751   3.293478    -1.88   0.060    -12.66085    .2493467
     period12 |  -7.380059   3.681868    -2.00   0.045    -14.59639   -.1637303
        lpopl |   -1.14761   .5640889    -2.03   0.042    -2.253204   -.0420164
     xmembers |   2.261487   1.053542     2.15   0.032     .1965829    4.326391
     lnregion |   .1458894   .3770986     0.39   0.699    -.5932104    .8849891
   repression |   .4804969    .820475     0.59   0.558    -1.127605    2.088598
  lreduration |  -.4432074   .7082787    -0.63   0.531    -1.831408    .9449934
        _cons |   15.18458   6.259344     2.43   0.015     2.916494    27.45267
--------------+----------------------------------------------------------------
     /lnsig2u |   3.323051   .6921573                      1.966448    4.679654
--------------+----------------------------------------------------------------
      sigma_u |    5.26734   1.822914                       2.67306    10.37944
          rho |    .893994   .0655948                      .6847311    .9703676
-------------------------------------------------------------------------------

.                 xi:xtlogit any_sec_defect xpers2 lt coldwar lpop xmembers lnre
> gion repress lreduration,fe
note: multiple positive outcomes within groups encountered.
note: 97 groups (251 obs) omitted because of all positive or
      all negative outcomes.

Iteration 0:   log likelihood = -16.144907  
Iteration 1:   log likelihood = -12.329131  
Iteration 2:   log likelihood = -12.137555  
Iteration 3:   log likelihood = -12.133587  
Iteration 4:   log likelihood = -12.133576  
Iteration 5:   log likelihood = -12.133576  

Conditional fixed-effects logistic regression        Number of obs    =     60
Group variable: gwf_caseid                           Number of groups =     18

                                                     Obs per group:
                                                                  min =      2
                                                                  avg =    3.3
                                                                  max =      9

                                                     LR chi2(8)       =  16.75
Log likelihood = -12.133576                          Prob > chi2      = 0.0328

-------------------------------------------------------------------------------
any_sec_def~t | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
       xpers2 |   1.336329   1.367182     0.98   0.328    -1.343297    4.015956
           lt |   .6341248   .7174213     0.88   0.377    -.7719952    2.040245
      coldwar |  -2.484323   2.699409    -0.92   0.357    -7.775067     2.80642
        lpopl |    -.33211    5.46246    -0.06   0.952    -11.03833    10.37411
     xmembers |   1.411408   .7749233     1.82   0.069    -.1074136     2.93023
     lnregion |   .4373067   .4881713     0.90   0.370    -.5194914    1.394105
   repression |   .2304678   1.237036     0.19   0.852    -2.194078    2.655014
  lreduration |  -.1044748   .9228146    -0.11   0.910    -1.913158    1.704209
-------------------------------------------------------------------------------

.                 estout def1 def2 def3 def4 using TableE1.tex, cells(b(star  fm
> t(%9.3f)) se(par fmt(%9.3f))) ///
>                 stats(r2 N N_clust) style(tex) replace label starlevels(* 0.05
> ) title(\label{tabE1})
(file TableE1.tex not found)
(output written to TableE1.tex)

.                 
.                 **** Alternative measures of personalization ****
.                 xi:logit any_sec_defect gwf_pers $x1,vce(cluster id)

Iteration 0:   log pseudolikelihood = -204.35784  
Iteration 1:   log pseudolikelihood = -161.91376  
Iteration 2:   log pseudolikelihood = -160.70386  
Iteration 3:   log pseudolikelihood = -160.69795  
Iteration 4:   log pseudolikelihood = -160.69795  

Logistic regression                                     Number of obs =    311
                                                        Wald chi2(18) =  62.46
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -160.69795                       Pseudo R2     = 0.2136

                                    (Std. err. adjusted for 178 clusters in id)
-------------------------------------------------------------------------------
              |               Robust
any_sec_def~t | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
 gwf_personal |   .7987289   .4531418     1.76   0.078    -.0894128    1.686871
           lt |   .0668448   .1655064     0.40   0.686    -.2575419    .3912314
      period2 |   1.279569   .9756137     1.31   0.190    -.6325986    3.191737
      period3 |   2.687243   .8514144     3.16   0.002     1.018501    4.355984
      period4 |   .0646655    1.21243     0.05   0.957    -2.311654    2.440985
      period5 |   2.272344   1.580405     1.44   0.150    -.8251934    5.369881
      period6 |   .1485008   1.221022     0.12   0.903    -2.244658     2.54166
      period7 |  -.2081994   1.188268    -0.18   0.861    -2.537161    2.120762
      period8 |   .1355437     1.0926     0.12   0.901    -2.005913       2.277
      period9 |   .1832581   1.216508     0.15   0.880    -2.201054     2.56757
     period10 |  -.1939409   1.226495    -0.16   0.874    -2.597828    2.209946
     period11 |  -.5831426   1.260862    -0.46   0.644    -3.054386    1.888101
     period12 |  -1.082803   1.205821    -0.90   0.369    -3.446169    1.280564
        lpopl |  -.5785807    .175956    -3.29   0.001    -.9234482   -.2337132
     xmembers |   .8034715   .2926797     2.75   0.006     .2298299    1.377113
     lnregion |    .011137   .1511619     0.07   0.941    -.2851348    .3074088
   repression |   .5288516   .2937411     1.80   0.072    -.0468704    1.104574
  lreduration |  -.5014487   .3219379    -1.56   0.119    -1.132435     .129538
        _cons |   4.795553    1.84624     2.60   0.009      1.17699    8.414117
-------------------------------------------------------------------------------

.                 est store def_p1

.                 xi:logit any_sec_defect xpers $x1,vce(cluster id)

Iteration 0:   log pseudolikelihood = -204.35784  
Iteration 1:   log pseudolikelihood = -156.99004  
Iteration 2:   log pseudolikelihood = -155.64665  
Iteration 3:   log pseudolikelihood = -155.63905  
Iteration 4:   log pseudolikelihood = -155.63905  

Logistic regression                                     Number of obs =    311
                                                        Wald chi2(18) =  72.25
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -155.63905                       Pseudo R2     = 0.2384

                                    (Std. err. adjusted for 178 clusters in id)
-------------------------------------------------------------------------------
              |               Robust
any_sec_def~t | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
        xpers |   .7075317   .2268569     3.12   0.002     .2629004    1.152163
           lt |  -.2944072    .195824    -1.50   0.133    -.6782152    .0894008
      period2 |   1.243861   .9491473     1.31   0.190    -.6164339    3.104155
      period3 |   2.967682   .8077662     3.67   0.000     1.384489    4.550874
      period4 |   .1534842   1.180186     0.13   0.897    -2.159638    2.466606
      period5 |   2.371651   1.495146     1.59   0.113    -.5587808    5.302082
      period6 |   -.091843    1.13322    -0.08   0.935    -2.312914    2.129228
      period7 |  -.4095733   1.191628    -0.34   0.731    -2.745122    1.925975
      period8 |   -.111792   1.061488    -0.11   0.916    -2.192269    1.968685
      period9 |  -.0952553   1.249113    -0.08   0.939    -2.543473    2.352962
     period10 |  -.6371045   1.177135    -0.54   0.588    -2.944247    1.670038
     period11 |   -.682431   1.166118    -0.59   0.558    -2.967981    1.603119
     period12 |  -1.250606   1.180159    -1.06   0.289    -3.563676    1.062463
        lpopl |  -.5372693   .1777554    -3.02   0.003    -.8856635    -.188875
     xmembers |   .8262598   .2769191     2.98   0.003     .2835083    1.369011
     lnregion |   .0200042   .1615237     0.12   0.901    -.2965765    .3365848
   repression |   .4972182   .2852005     1.74   0.081    -.0617645    1.056201
  lreduration |  -.5541936   .3218334    -1.72   0.085    -1.184975    .0765883
        _cons |   4.906043   1.837571     2.67   0.008      1.30447    8.507615
-------------------------------------------------------------------------------

.                 est store def_p2

.                 xi:logit any_sec_defect xpers1 $x1,vce(cluster id)

Iteration 0:   log pseudolikelihood = -204.35784  
Iteration 1:   log pseudolikelihood =  -162.3336  
Iteration 2:   log pseudolikelihood = -161.08157  
Iteration 3:   log pseudolikelihood = -161.07525  
Iteration 4:   log pseudolikelihood = -161.07525  

Logistic regression                                     Number of obs =    311
                                                        Wald chi2(18) =  64.55
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -161.07525                       Pseudo R2     = 0.2118

                                    (Std. err. adjusted for 178 clusters in id)
-------------------------------------------------------------------------------
              |               Robust
any_sec_def~t | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
       xpers1 |   .3315489   .2135049     1.55   0.120    -.0869131    .7500109
           lt |  -.0406799   .1961118    -0.21   0.836    -.4250519    .3436922
      period2 |   1.183459   1.072089     1.10   0.270    -.9177966    3.284715
      period3 |   2.774783   .9249138     3.00   0.003     .9619856    4.587581
      period4 |  -.0460185   1.288398    -0.04   0.972    -2.571232    2.479195
      period5 |   2.156431    1.64411     1.31   0.190    -1.065965    5.378827
      period6 |  -.0572059   1.312442    -0.04   0.965    -2.629546    2.515134
      period7 |  -.3238392   1.309334    -0.25   0.805    -2.890087    2.242408
      period8 |  -.0188032   1.217399    -0.02   0.988    -2.404861    2.367255
      period9 |  -.0409781   1.344361    -0.03   0.976    -2.675877    2.593921
     period10 |  -.1653719   1.362956    -0.12   0.903    -2.836716    2.505972
     period11 |  -.3355805   1.315768    -0.26   0.799    -2.914438    2.243277
     period12 |  -.9178336   1.315163    -0.70   0.485    -3.495507    1.659839
        lpopl |  -.6087836   .1780096    -3.42   0.001     -.957676   -.2598911
     xmembers |   .7918013   .2928262     2.70   0.007     .2178725     1.36573
     lnregion |   .0428132   .1563002     0.27   0.784    -.2635295     .349156
   repression |   .5308159   .2885874     1.84   0.066     -.034805    1.096437
  lreduration |   -.545516    .333067    -1.64   0.101    -1.198315    .1072834
        _cons |   5.394405   1.957663     2.76   0.006     1.557455    9.231355
-------------------------------------------------------------------------------

.                 est store def_p3

.                 xi:logit any_sec_defect xpers2 $x1,vce(cluster id)

Iteration 0:   log pseudolikelihood = -204.35784  
Iteration 1:   log pseudolikelihood = -159.64575  
Iteration 2:   log pseudolikelihood = -158.11176  
Iteration 3:   log pseudolikelihood = -158.09613  
Iteration 4:   log pseudolikelihood = -158.09612  

Logistic regression                                     Number of obs =    311
                                                        Wald chi2(18) =  74.81
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -158.09612                       Pseudo R2     = 0.2264

                                    (Std. err. adjusted for 178 clusters in id)
-------------------------------------------------------------------------------
              |               Robust
any_sec_def~t | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
       xpers2 |   .5335284   .2401067     2.22   0.026     .0629279    1.004129
           lt |  -.1887852   .2006929    -0.94   0.347    -.5821361    .2045657
      period2 |   1.387888   .9229746     1.50   0.133    -.4211085    3.196886
      period3 |   2.981817    .837637     3.56   0.000     1.340079    4.623556
      period4 |   .4456636   1.250277     0.36   0.722    -2.004834    2.896161
      period5 |   2.479951   1.393913     1.78   0.075    -.2520685    5.211971
      period6 |   .2745424   1.171707     0.23   0.815     -2.02196    2.571045
      period7 |  -.0789263   1.203285    -0.07   0.948    -2.437322     2.27947
      period8 |   .1257563   1.085577     0.12   0.908    -2.001935    2.253448
      period9 |   .4268899   1.241854     0.34   0.731    -2.007099    2.860879
     period10 |   -.218805   1.164084    -0.19   0.851    -2.500368    2.062758
     period11 |   -.256396   1.187592    -0.22   0.829    -2.584033    2.071241
     period12 |  -.9082888    1.17658    -0.77   0.440    -3.214343    1.397765
        lpopl |  -.5339994   .1823531    -2.93   0.003    -.8914049   -.1765939
     xmembers |   .7959461   .2610828     3.05   0.002     .2842333    1.307659
     lnregion |   .0136748   .1545529     0.09   0.929    -.2892433    .3165929
   repression |   .4223964   .2868347     1.47   0.141    -.1397894    .9845821
  lreduration |   -.626639   .3093922    -2.03   0.043    -1.233037   -.0202415
        _cons |   4.575364   1.873772     2.44   0.015     .9028381    8.247889
-------------------------------------------------------------------------------

.                 est store def_p4

.                 xi:logit any_sec_defect xpers1 xpers2 $x1,vce(cluster id)

Iteration 0:   log pseudolikelihood = -204.35784  
Iteration 1:   log pseudolikelihood = -158.17861  
Iteration 2:   log pseudolikelihood =  -156.5637  
Iteration 3:   log pseudolikelihood = -156.54795  
Iteration 4:   log pseudolikelihood = -156.54794  

Logistic regression                                     Number of obs =    311
                                                        Wald chi2(19) =  75.21
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -156.54794                       Pseudo R2     = 0.2340

                                    (Std. err. adjusted for 178 clusters in id)
-------------------------------------------------------------------------------
              |               Robust
any_sec_def~t | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
       xpers1 |   .2830364   .2153508     1.31   0.189    -.1390435    .7051162
       xpers2 |   .5057996   .2484451     2.04   0.042     .0188562     .992743
           lt |  -.2828451   .1987851    -1.42   0.155    -.6724567    .1067666
      period2 |   1.384998    .941423     1.47   0.141     -.460157    3.230153
      period3 |   3.027389   .8496132     3.56   0.000     1.362178      4.6926
      period4 |   .4429788   1.241565     0.36   0.721    -1.990445    2.876402
      period5 |   2.478851   1.429587     1.73   0.083     -.323089     5.28079
      period6 |    .204743   1.180852     0.17   0.862    -2.109684     2.51917
      period7 |  -.1545051   1.240061    -0.12   0.901     -2.58498     2.27597
      period8 |   .1253154    1.11271     0.11   0.910    -2.055557    2.306188
      period9 |   .2140109   1.276637     0.17   0.867    -2.288151    2.716173
     period10 |  -.2778105   1.220422    -0.23   0.820    -2.669793    2.114172
     period11 |  -.2265055   1.217649    -0.19   0.852    -2.613055    2.160044
     period12 |  -.9204656   1.206833    -0.76   0.446    -3.285814    1.444883
        lpopl |  -.5364078    .183102    -2.93   0.003    -.8952812   -.1775345
     xmembers |   .7869849    .269064     2.92   0.003     .2596292    1.314341
     lnregion |   .0256289   .1568254     0.16   0.870    -.2817432    .3330009
   repression |   .4481737   .2877702     1.56   0.119    -.1158455    1.012193
  lreduration |  -.5879187   .3206622    -1.83   0.067    -1.216405    .0405676
        _cons |   4.617288   1.902334     2.43   0.015     .8887811    8.345795
-------------------------------------------------------------------------------

.                 est store def_p5

. 
. 
.         **********************************************************************
>  
.         ******************     Democratization outcomes     ******************
.         **********************************************************************
.         use temp-fe,clear

.         tab gdem gwf_case_fail

           |  Regime-case failure
      gdem |         0          1 |     Total
-----------+----------------------+----------
         0 |     4,336        120 |     4,456 
         1 |         0        103 |       103 
-----------+----------------------+----------
     Total |     4,336        223 |     4,559 

.         forval i =1/12{
  2.                 egen m_period`i'=mean(period`i'),by(gwf_caseid)
  3.         }

.         
.         * show within transform equivalency *
.         qui reghdfe gdem ld lnregion xpers2,a(gwf_caseid  year)cluster(gwf_cas
> eid)

.         lincom xpers2

 ( 1)  xpers2 = 0

------------------------------------------------------------------------------
        gdem | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0231457    .006422    -3.60   0.000    -.0357926   -.0104989
------------------------------------------------------------------------------

.         est store dem1

.         xtsum gdem if e(sample)==1

Variable         |      Mean   Std. dev.       Min        Max |    Observations
-----------------+--------------------------------------------+----------------
gdem     overall |  .0196251   .1387236          0          1 |     N =    4535
         between |             .0376109          0   .2142857 |     n =     117
         within  |              .135943  -.1946606   1.003752 | T-bar = 38.7607

.         qui reghdfe gdem ld lnregion period* xpers2,a(gwf_caseid)cluster(gwf_c
> aseid)

.         lincom xpers2

 ( 1)  xpers2 = 0

------------------------------------------------------------------------------
        gdem | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0240392   .0065177    -3.69   0.000    -.0368745   -.0112038
------------------------------------------------------------------------------

.         qui xtreg gdem ld lnregion period* xpers2,fe i(gwf_caseid)cluster(gwf_
> caseid)

.         lincom xpers2

 ( 1)  xpers2 = 0

------------------------------------------------------------------------------
        gdem | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0240392   .0065165    -3.69   0.000     -.036867   -.0112113
------------------------------------------------------------------------------

.         qui reg gdem ld lnregion period* xpers2 m_ld m_lnregion m_coldwar m_xp
> ers2 m_gdem m_period*,cluster(gwf_caseid)

.         lincom xpers2

 ( 1)  xpers2 = 0

------------------------------------------------------------------------------
        gdem | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0240392   .0065288    -3.68   0.000    -.0368911   -.0111873
------------------------------------------------------------------------------

.         qui reg gdem ld lnregion period* xpers2 m_ld m_lnregion m_coldwar m_xp
> ers2 m_period*,cluster(gwf_caseid)

.         lincom xpers2   

 ( 1)  xpers2 = 0

------------------------------------------------------------------------------
        gdem | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0240392    .006528    -3.68   0.000    -.0368896   -.0111887
------------------------------------------------------------------------------

.  
.           * CRE * Mundlak and Chamberlain within transformation, See Wooldridg
> e 2002
.         qui meprobit gdem ld lnregion period* xpers2 m_ld m_lnregion m_xpers2 
> m_period* || gwf_caseid: ,vce(cluster gwf_caseid)

.         margins,dydx(xpers2) 

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Marginal predicted mean, predict()
dy/dx wrt:  xpers2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0222292   .0091571    -2.43   0.015    -.0401767   -.0042817
------------------------------------------------------------------------------

.         est store dem2

.         xtsum gdem if e(sample)==1

Variable         |      Mean   Std. dev.       Min        Max |    Observations
-----------------+--------------------------------------------+----------------
gdem     overall |  .0225927   .1486173          0          1 |     N =    4559
         between |             .2333069          0          1 |     n =     280
         within  |             .1290103  -.4774073   1.004411 | T-bar = 16.2821

.         qui probit gdem ld lnregion period* xpers2 m_ld m_lnregion m_xpers2  m
> _period*,cluster(gwf_caseid)

.         margins,dydx(xpers2)

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(gdem), predict()
dy/dx wrt:  xpers2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0192897   .0042454    -4.54   0.000    -.0276106   -.0109688
------------------------------------------------------------------------------

.         
.         * Add lagged trend in democracy *
.         reghdfe gdem l1v2x_pol l2v2x_pol ld lnregion xpers2,a(gwf_caseid year)
> cluster(gwf_caseid)
(dropped 24 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =      4,519
Absorbing 2 HDFE groups                           F(   5,    255) =      11.74
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1878
                                                  Adj R-squared   =     0.1248
                                                  Within R-sq.    =     0.0248
Number of clusters (gwf_caseid) =        256      Root MSE        =     0.1300

                            (Std. err. adjusted for 256 clusters in gwf_caseid)
-------------------------------------------------------------------------------
              |               Robust
         gdem | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
l1v2x_polya~y |   .3671173   .0894943     4.10   0.000     .1908752    .5433594
l2v2x_polya~y |  -.1697526   .0773305    -2.20   0.029    -.3220404   -.0174648
           ld |   .0303974   .0057659     5.27   0.000     .0190426    .0417522
     lnregion |   .0032928   .0033268     0.99   0.323    -.0032588    .0098444
       xpers2 |  -.0214274    .006512    -3.29   0.001    -.0342516   -.0086032
        _cons |   -.095592   .0171081    -5.59   0.000    -.1292832   -.0619009
-------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  gwf_caseid |       256         256           0    *|
        year |        65           0          65     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.         est store dem3

.         xtsum gdem if e(sample)==1

Variable         |      Mean   Std. dev.       Min        Max |    Observations
-----------------+--------------------------------------------+----------------
gdem     overall |  .0196946   .1389641          0          1 |     N =    4519
         between |             .1110271          0         .5 |     n =     256
         within  |             .1295723  -.4803054   1.001513 | T-bar = 17.6523

.                 * Check with 3 & 4 year lags *
.         reghdfe gdem l1v2x_pol l2v2x_pol l3v2x_pol l4v2x_pol ld lnregion xpers
> 2,a(gwf_caseid year)cluster(gwf_caseid)
(dropped 24 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =      4,486
Absorbing 2 HDFE groups                           F(   7,    253) =       8.98
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1898
                                                  Adj R-squared   =     0.1265
                                                  Within R-sq.    =     0.0263
Number of clusters (gwf_caseid) =        254      Root MSE        =     0.1303

                            (Std. err. adjusted for 254 clusters in gwf_caseid)
-------------------------------------------------------------------------------
              |               Robust
         gdem | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
l1v2x_polya~y |   .3838527   .0925297     4.15   0.000     .2016261    .5660794
l2v2x_polya~y |  -.2336992   .1239049    -1.89   0.060    -.4777156    .0103173
l3v2x_polya~y |   .1428485   .1350484     1.06   0.291    -.1231136    .4088107
l4v2x_polya~y |  -.1054276   .0815538    -1.29   0.197    -.2660383    .0551832
           ld |   .0308544   .0058248     5.30   0.000     .0193831    .0423257
     lnregion |   .0033119   .0033418     0.99   0.323    -.0032693    .0098931
       xpers2 |  -.0223421   .0065793    -3.40   0.001    -.0352992    -.009385
        _cons |  -.0948982    .016918    -5.61   0.000    -.1282163   -.0615801
-------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  gwf_caseid |       254         254           0    *|
        year |        65           0          65     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.         egen m_l1v2x_pol=mean(l1v2x_pol) if sample==1,by(gwf_caseid)

.         egen m_l2v2x_pol=mean(l2v2x_pol) if sample==1,by(gwf_caseid)

.         meprobit gdem l1v2x_pol l2v2x_pol ld lnregion period* xpers2 ///
>                 m_ld m_lnregion m_xpers2 m_l1v2x_pol m_l2v2x_pol m_period* ///
>                 || gwf_caseid: ,vce(cluster gwf_caseid)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -589.62153  
Iteration 1:   log likelihood = -317.08341  
Iteration 2:   log likelihood = -263.75476  
Iteration 3:   log likelihood = -246.43204  
Iteration 4:   log likelihood = -245.98002  
Iteration 5:   log likelihood = -245.97837  
Iteration 6:   log likelihood = -245.97837  

Refining starting values:

Grid node 0:   log likelihood =  -267.5761

Fitting full model:

Iteration 0:   log pseudolikelihood =  -267.5761  (not concave)
Iteration 1:   log pseudolikelihood = -255.84191  (not concave)
Iteration 2:   log pseudolikelihood = -246.83694  
Iteration 3:   log pseudolikelihood = -244.99153  
Iteration 4:   log pseudolikelihood = -244.92692  
Iteration 5:   log pseudolikelihood = -244.92412  
Iteration 6:   log pseudolikelihood = -244.92411  

Mixed-effects probit regression                 Number of obs     =      4,543
Group variable: gwf_caseid                      Number of groups  =        280

                                                Obs per group:
                                                              min =          1
                                                              avg =       16.2
                                                              max =         65

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(34)     =      57.41
Log pseudolikelihood = -244.92411               Prob > chi2       =     0.0073
                            (Std. err. adjusted for 280 clusters in gwf_caseid)
-------------------------------------------------------------------------------
              |               Robust
         gdem | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
l1v2x_polya~y |   5.157361   2.324017     2.22   0.026     .6023708    9.712351
l2v2x_polya~y |   .5488981   1.657439     0.33   0.741    -2.699622    3.797418
           ld |    3.34908    1.26295     2.65   0.008     .8737442    5.824415
     lnregion |   .1005485   .0693134     1.45   0.147    -.0353031    .2364002
      period1 |  -.7126251   1.046958    -0.68   0.496    -2.764626    1.339375
      period2 |   2.089436    1.69019     1.24   0.216    -1.223276    5.402148
      period3 |   3.010889    1.75651     1.71   0.087    -.4318075    6.453585
      period4 |   2.764104    1.82604     1.51   0.130     -.814868    6.343076
      period5 |   4.438468   2.042328     2.17   0.030     .4355794    8.441358
      period6 |   5.082782    2.13625     2.38   0.017      .895809    9.269756
      period7 |   5.122323   2.209481     2.32   0.020     .7918204    9.452825
      period8 |   6.168336   2.349754     2.63   0.009     1.562902    10.77377
      period9 |   6.935037   2.700528     2.57   0.010     1.642099    12.22798
     period10 |   6.521221    2.66541     2.45   0.014     1.297114    11.74533
     period11 |   6.131694   2.677833     2.29   0.022     .8832378    11.38015
     period12 |   5.229581   2.632841     1.99   0.047     .0693073    10.38985
       xpers2 |  -.8238653   .3380634    -2.44   0.015    -1.486457   -.1612732
         m_ld |  -5.286317   1.735099    -3.05   0.002    -8.687049   -1.885586
   m_lnregion |   -.115699   .2722622    -0.42   0.671    -.6493231    .4179251
     m_xpers2 |   .5918907   .2769113     2.14   0.033     .0491545    1.134627
  m_l1v2x_pol |  -15.89666   5.726873    -2.78   0.006    -27.12113     -4.6722
  m_l2v2x_pol |   10.37208   4.344176     2.39   0.017     1.857657    18.88651
    m_period1 |  -3.283539   1.788301    -1.84   0.066    -6.788545    .2214664
    m_period2 |  -6.543956   2.781693    -2.35   0.019    -11.99597   -1.091939
    m_period3 |   -6.48506   2.497767    -2.60   0.009    -11.38059   -1.589527
    m_period4 |  -6.893361   2.663948    -2.59   0.010     -12.1146   -1.672119
    m_period5 |  -8.576802   3.023087    -2.84   0.005    -14.50194   -2.651661
    m_period6 |  -8.539591    3.09995    -2.75   0.006    -14.61538   -2.463801
    m_period7 |  -8.775213   3.147706    -2.79   0.005     -14.9446   -2.605823
    m_period8 |  -8.761414   2.982676    -2.94   0.003    -14.60735   -2.915476
    m_period9 |  -9.553118   3.467685    -2.75   0.006    -16.34966   -2.756581
   m_period10 |  -9.410574   3.343481    -2.81   0.005    -15.96368   -2.857471
   m_period11 |  -8.055982   3.181205    -2.53   0.011    -14.29103   -1.820936
   m_period12 |  -9.145682    3.36602    -2.72   0.007    -15.74296   -2.548403
        _cons |   1.968433   1.078471     1.83   0.068     -.145331    4.082196
--------------+----------------------------------------------------------------
gwf_caseid    |
    var(_cons)|   .4682222    .724525                      .0225583    9.718461
-------------------------------------------------------------------------------

.         xtsum gdem if e(sample)==1

Variable         |      Mean   Std. dev.       Min        Max |    Observations
-----------------+--------------------------------------------+----------------
gdem     overall |  .0226722   .1488727          0          1 |     N =    4543
         between |             .2333067          0          1 |     n =     280
         within  |             .1292295  -.4773278    1.00449 | T-bar =  16.225

.         est store dem4

.         margins,dydx(xpers2) 

Average marginal effects                                 Number of obs = 4,543
Model VCE: Robust

Expression: Marginal predicted mean, predict()
dy/dx wrt:  xpers2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0212452    .006623    -3.21   0.001    -.0342261   -.0082644
------------------------------------------------------------------------------

.         
.         * Interactive fixed effects *
.         regife gdem ld lnregion xpers2 ,a(gwf_caseid year) factor(gwf_caseid y
> ear,1) vce(cluster gwf_caseid)
The option factors() was renamed to ife(). In the future, please use the syntax 
> ife(gwf_caseid year,1) to specify the factor model.
The algorithm did not converge : convergence error is 7.4e-08 (tolerance 1.0e-09
> )
Allow for more iterations with the option maxiter

REGIFE                                            Number of obs   =       4535
Panel structure: gwf_caseid, year                 F(   3,    255) =       6.61
Factor dimension: 1                               Prob > F        =     0.0003
Converged: false                                  Root MSE        =     0.0736
                                                  Iterations      =      10000
------------------------------------------------------------------------------
        gdem | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
          ld |   .0116457   .0030083     3.87   0.000     .0057215      .01757
    lnregion |   .0034435   .0018634     1.85   0.066     -.000226     .007113
      xpers2 |  -.0093572   .0036771    -2.54   0.012    -.0165987   -.0021158
       _cons |  -.0094099   .0075022    -1.25   0.211     -.024184    .0053643
------------------------------------------------------------------------------

.         est store dem5

.         
.         * Nonlinear case-specific time trends *
.         xi:reg gdem i.gwf_caseid*time i.gwf_caseid*time2 ld lnregion xpers2 , 
>  vce(cluster gwf_leaderid)
i.gwf_caseid      _Igwf_casei_1-280   (naturally coded; _Igwf_casei_1 omitted)
i.gwf_ca~d*time   _IgwfXtim_#         (coded as above)
i.gwf_c~d*time2   _IgwfXtima#         (coded as above)
note: _Igwf_casei_2 omitted because of collinearity.
note: _Igwf_casei_3 omitted because of collinearity.
note: _Igwf_casei_4 omitted because of collinearity.
note: _Igwf_casei_5 omitted because of collinearity.
note: _Igwf_casei_6 omitted because of collinearity.
note: _Igwf_casei_7 omitted because of collinearity.
note: _Igwf_casei_12 omitted because of collinearity.
note: _Igwf_casei_14 omitted because of collinearity.
note: _Igwf_casei_16 omitted because of collinearity.
note: _Igwf_casei_17 omitted because of collinearity.
note: _Igwf_casei_18 omitted because of collinearity.
note: _Igwf_casei_19 omitted because of collinearity.
note: _Igwf_casei_20 omitted because of collinearity.
note: _Igwf_casei_22 omitted because of collinearity.
note: _Igwf_casei_23 omitted because of collinearity.
note: _Igwf_casei_24 omitted because of collinearity.
note: _Igwf_casei_26 omitted because of collinearity.
note: _Igwf_casei_27 omitted because of collinearity.
note: _Igwf_casei_28 omitted because of collinearity.
note: _Igwf_casei_29 omitted because of collinearity.
note: _Igwf_casei_30 omitted because of collinearity.
note: _Igwf_casei_31 omitted because of collinearity.
note: _Igwf_casei_33 omitted because of collinearity.
note: _Igwf_casei_34 omitted because of collinearity.
note: _Igwf_casei_37 omitted because of collinearity.
note: _Igwf_casei_39 omitted because of collinearity.
note: _Igwf_casei_43 omitted because of collinearity.
note: _Igwf_casei_45 omitted because of collinearity.
note: _Igwf_casei_48 omitted because of collinearity.
note: _Igwf_casei_49 omitted because of collinearity.
note: _Igwf_casei_50 omitted because of collinearity.
note: _Igwf_casei_51 omitted because of collinearity.
note: _Igwf_casei_52 omitted because of collinearity.
note: _Igwf_casei_53 omitted because of collinearity.
note: _Igwf_casei_54 omitted because of collinearity.
note: _Igwf_casei_55 omitted because of collinearity.
note: _Igwf_casei_56 omitted because of collinearity.
note: _Igwf_casei_59 omitted because of collinearity.
note: _Igwf_casei_60 omitted because of collinearity.
note: _Igwf_casei_61 omitted because of collinearity.
note: _Igwf_casei_62 omitted because of collinearity.
note: _Igwf_casei_64 omitted because of collinearity.
note: _Igwf_casei_66 omitted because of collinearity.
note: _Igwf_casei_67 omitted because of collinearity.
note: _Igwf_casei_68 omitted because of collinearity.
note: _Igwf_casei_69 omitted because of collinearity.
note: _Igwf_casei_70 omitted because of collinearity.
note: _Igwf_casei_72 omitted because of collinearity.
note: _Igwf_casei_74 omitted because of collinearity.
note: _Igwf_casei_76 omitted because of collinearity.
note: _Igwf_casei_77 omitted because of collinearity.
note: _Igwf_casei_80 omitted because of collinearity.
note: _Igwf_casei_81 omitted because of collinearity.
note: _Igwf_casei_82 omitted because of collinearity.
note: _Igwf_casei_84 omitted because of collinearity.
note: _Igwf_casei_86 omitted because of collinearity.
note: _Igwf_casei_90 omitted because of collinearity.
note: _Igwf_casei_95 omitted because of collinearity.
note: _Igwf_casei_97 omitted because of collinearity.
note: _Igwf_casei_98 omitted because of collinearity.
note: _Igwf_casei_100 omitted because of collinearity.
note: _Igwf_casei_102 omitted because of collinearity.
note: _Igwf_casei_105 omitted because of collinearity.
note: _Igwf_casei_109 omitted because of collinearity.
note: _Igwf_casei_110 omitted because of collinearity.
note: _Igwf_casei_112 omitted because of collinearity.
note: _Igwf_casei_113 omitted because of collinearity.
note: _Igwf_casei_114 omitted because of collinearity.
note: _Igwf_casei_116 omitted because of collinearity.
note: _Igwf_casei_117 omitted because of collinearity.
note: _Igwf_casei_119 omitted because of collinearity.
note: _Igwf_casei_121 omitted because of collinearity.
note: _Igwf_casei_122 omitted because of collinearity.
note: _Igwf_casei_123 omitted because of collinearity.
note: _Igwf_casei_124 omitted because of collinearity.
note: _Igwf_casei_125 omitted because of collinearity.
note: _Igwf_casei_126 omitted because of collinearity.
note: _Igwf_casei_127 omitted because of collinearity.
note: _Igwf_casei_128 omitted because of collinearity.
note: _Igwf_casei_131 omitted because of collinearity.
note: _Igwf_casei_133 omitted because of collinearity.
note: _Igwf_casei_134 omitted because of collinearity.
note: _Igwf_casei_135 omitted because of collinearity.
note: _Igwf_casei_138 omitted because of collinearity.
note: _Igwf_casei_142 omitted because of collinearity.
note: _Igwf_casei_144 omitted because of collinearity.
note: _Igwf_casei_147 omitted because of collinearity.
note: _Igwf_casei_148 omitted because of collinearity.
note: _Igwf_casei_149 omitted because of collinearity.
note: _Igwf_casei_152 omitted because of collinearity.
note: _Igwf_casei_153 omitted because of collinearity.
note: _Igwf_casei_162 omitted because of collinearity.
note: _Igwf_casei_168 omitted because of collinearity.
note: _Igwf_casei_170 omitted because of collinearity.
note: _Igwf_casei_171 omitted because of collinearity.
note: _Igwf_casei_172 omitted because of collinearity.
note: _Igwf_casei_174 omitted because of collinearity.
note: _Igwf_casei_178 omitted because of collinearity.
note: _Igwf_casei_180 omitted because of collinearity.
note: _Igwf_casei_181 omitted because of collinearity.
note: _Igwf_casei_185 omitted because of collinearity.
note: _Igwf_casei_186 omitted because of collinearity.
note: _Igwf_casei_194 omitted because of collinearity.
note: _Igwf_casei_195 omitted because of collinearity.
note: _Igwf_casei_196 omitted because of collinearity.
note: _Igwf_casei_197 omitted because of collinearity.
note: _Igwf_casei_199 omitted because of collinearity.
note: _Igwf_casei_200 omitted because of collinearity.
note: _Igwf_casei_202 omitted because of collinearity.
note: _Igwf_casei_205 omitted because of collinearity.
note: _Igwf_casei_207 omitted because of collinearity.
note: _Igwf_casei_208 omitted because of collinearity.
note: _Igwf_casei_209 omitted because of collinearity.
note: _Igwf_casei_210 omitted because of collinearity.
note: _Igwf_casei_212 omitted because of collinearity.
note: _Igwf_casei_214 omitted because of collinearity.
note: _Igwf_casei_220 omitted because of collinearity.
note: _Igwf_casei_221 omitted because of collinearity.
note: _Igwf_casei_222 omitted because of collinearity.
note: _Igwf_casei_223 omitted because of collinearity.
note: _Igwf_casei_224 omitted because of collinearity.
note: _Igwf_casei_225 omitted because of collinearity.
note: _Igwf_casei_230 omitted because of collinearity.
note: _Igwf_casei_231 omitted because of collinearity.
note: _Igwf_casei_235 omitted because of collinearity.
note: _Igwf_casei_236 omitted because of collinearity.
note: _Igwf_casei_238 omitted because of collinearity.
note: _Igwf_casei_239 omitted because of collinearity.
note: _Igwf_casei_240 omitted because of collinearity.
note: _Igwf_casei_242 omitted because of collinearity.
note: _Igwf_casei_243 omitted because of collinearity.
note: _Igwf_casei_246 omitted because of collinearity.
note: _Igwf_casei_247 omitted because of collinearity.
note: _Igwf_casei_248 omitted because of collinearity.
note: _Igwf_casei_251 omitted because of collinearity.
note: _Igwf_casei_252 omitted because of collinearity.
note: _Igwf_casei_253 omitted because of collinearity.
note: _Igwf_casei_255 omitted because of collinearity.
note: _Igwf_casei_256 omitted because of collinearity.
note: _Igwf_casei_257 omitted because of collinearity.
note: _Igwf_casei_259 omitted because of collinearity.
note: _Igwf_casei_260 omitted because of collinearity.
note: _Igwf_casei_262 omitted because of collinearity.
note: _Igwf_casei_263 omitted because of collinearity.
note: _Igwf_casei_264 omitted because of collinearity.
note: _Igwf_casei_268 omitted because of collinearity.
note: _Igwf_casei_274 omitted because of collinearity.
note: _Igwf_casei_278 omitted because of collinearity.
note: _Igwf_casei_279 omitted because of collinearity.
note: _IgwfXtim_10 omitted because of collinearity.
note: _IgwfXtim_18 omitted because of collinearity.
note: _IgwfXtim_20 omitted because of collinearity.
note: _IgwfXtim_30 omitted because of collinearity.
note: _IgwfXtim_32 omitted because of collinearity.
note: _IgwfXtim_34 omitted because of collinearity.
note: _IgwfXtim_77 omitted because of collinearity.
note: _IgwfXtim_84 omitted because of collinearity.
note: _IgwfXtim_100 omitted because of collinearity.
note: _IgwfXtim_117 omitted because of collinearity.
note: _IgwfXtim_120 omitted because of collinearity.
note: _IgwfXtim_142 omitted because of collinearity.
note: _IgwfXtim_152 omitted because of collinearity.
note: _IgwfXtim_162 omitted because of collinearity.
note: _IgwfXtim_207 omitted because of collinearity.
note: _IgwfXtim_221 omitted because of collinearity.
note: _IgwfXtim_224 omitted because of collinearity.
note: _IgwfXtim_236 omitted because of collinearity.
note: _IgwfXtim_239 omitted because of collinearity.
note: _IgwfXtim_242 omitted because of collinearity.
note: _IgwfXtim_243 omitted because of collinearity.
note: _IgwfXtim_248 omitted because of collinearity.
note: _IgwfXtim_249 omitted because of collinearity.
note: _IgwfXtim_253 omitted because of collinearity.
note: _IgwfXtim_259 omitted because of collinearity.
note: _Igwf_casei_8 omitted because of collinearity.
note: _Igwf_casei_9 omitted because of collinearity.
note: _Igwf_casei_10 omitted because of collinearity.
note: _Igwf_casei_11 omitted because of collinearity.
note: _Igwf_casei_13 omitted because of collinearity.
note: _Igwf_casei_15 omitted because of collinearity.
note: _Igwf_casei_18 omitted because of collinearity.
note: _Igwf_casei_20 omitted because of collinearity.
note: _Igwf_casei_21 omitted because of collinearity.
note: _Igwf_casei_24 omitted because of collinearity.
note: _Igwf_casei_25 omitted because of collinearity.
note: _Igwf_casei_27 omitted because of collinearity.
note: _Igwf_casei_28 omitted because of collinearity.
note: _Igwf_casei_29 omitted because of collinearity.
note: _Igwf_casei_30 omitted because of collinearity.
note: _Igwf_casei_32 omitted because of collinearity.
note: _Igwf_casei_34 omitted because of collinearity.
note: _Igwf_casei_35 omitted because of collinearity.
note: _Igwf_casei_36 omitted because of collinearity.
note: _Igwf_casei_37 omitted because of collinearity.
note: _Igwf_casei_38 omitted because of collinearity.
note: _Igwf_casei_39 omitted because of collinearity.
note: _Igwf_casei_40 omitted because of collinearity.
note: _Igwf_casei_41 omitted because of collinearity.
note: _Igwf_casei_42 omitted because of collinearity.
note: _Igwf_casei_44 omitted because of collinearity.
note: _Igwf_casei_45 omitted because of collinearity.
note: _Igwf_casei_46 omitted because of collinearity.
note: _Igwf_casei_47 omitted because of collinearity.
note: _Igwf_casei_57 omitted because of collinearity.
note: _Igwf_casei_58 omitted because of collinearity.
note: _Igwf_casei_61 omitted because of collinearity.
note: _Igwf_casei_63 omitted because of collinearity.
note: _Igwf_casei_65 omitted because of collinearity.
note: _Igwf_casei_71 omitted because of collinearity.
note: _Igwf_casei_73 omitted because of collinearity.
note: _Igwf_casei_75 omitted because of collinearity.
note: _Igwf_casei_77 omitted because of collinearity.
note: _Igwf_casei_78 omitted because of collinearity.
note: _Igwf_casei_79 omitted because of collinearity.
note: _Igwf_casei_82 omitted because of collinearity.
note: _Igwf_casei_83 omitted because of collinearity.
note: _Igwf_casei_85 omitted because of collinearity.
note: _Igwf_casei_86 omitted because of collinearity.
note: _Igwf_casei_87 omitted because of collinearity.
note: _Igwf_casei_88 omitted because of collinearity.
note: _Igwf_casei_89 omitted because of collinearity.
note: _Igwf_casei_91 omitted because of collinearity.
note: _Igwf_casei_92 omitted because of collinearity.
note: _Igwf_casei_93 omitted because of collinearity.
note: _Igwf_casei_94 omitted because of collinearity.
note: _Igwf_casei_96 omitted because of collinearity.
note: _Igwf_casei_99 omitted because of collinearity.
note: _Igwf_casei_100 omitted because of collinearity.
note: _Igwf_casei_101 omitted because of collinearity.
note: _Igwf_casei_103 omitted because of collinearity.
note: _Igwf_casei_104 omitted because of collinearity.
note: _Igwf_casei_106 omitted because of collinearity.
note: _Igwf_casei_107 omitted because of collinearity.
note: _Igwf_casei_108 omitted because of collinearity.
note: _Igwf_casei_111 omitted because of collinearity.
note: _Igwf_casei_114 omitted because of collinearity.
note: _Igwf_casei_115 omitted because of collinearity.
note: _Igwf_casei_117 omitted because of collinearity.
note: _Igwf_casei_118 omitted because of collinearity.
note: _Igwf_casei_120 omitted because of collinearity.
note: _Igwf_casei_123 omitted because of collinearity.
note: _Igwf_casei_124 omitted because of collinearity.
note: _Igwf_casei_129 omitted because of collinearity.
note: _Igwf_casei_130 omitted because of collinearity.
note: _Igwf_casei_132 omitted because of collinearity.
note: _Igwf_casei_136 omitted because of collinearity.
note: _Igwf_casei_137 omitted because of collinearity.
note: _Igwf_casei_139 omitted because of collinearity.
note: _Igwf_casei_140 omitted because of collinearity.
note: _Igwf_casei_141 omitted because of collinearity.
note: _Igwf_casei_142 omitted because of collinearity.
note: _Igwf_casei_143 omitted because of collinearity.
note: _Igwf_casei_145 omitted because of collinearity.
note: _Igwf_casei_146 omitted because of collinearity.
note: _Igwf_casei_150 omitted because of collinearity.
note: _Igwf_casei_151 omitted because of collinearity.
note: _Igwf_casei_152 omitted because of collinearity.
note: _Igwf_casei_153 omitted because of collinearity.
note: _Igwf_casei_154 omitted because of collinearity.
note: _Igwf_casei_155 omitted because of collinearity.
note: _Igwf_casei_156 omitted because of collinearity.
note: _Igwf_casei_157 omitted because of collinearity.
note: _Igwf_casei_158 omitted because of collinearity.
note: _Igwf_casei_159 omitted because of collinearity.
note: _Igwf_casei_160 omitted because of collinearity.
note: _Igwf_casei_161 omitted because of collinearity.
note: _Igwf_casei_162 omitted because of collinearity.
note: _Igwf_casei_163 omitted because of collinearity.
note: _Igwf_casei_164 omitted because of collinearity.
note: _Igwf_casei_165 omitted because of collinearity.
note: _Igwf_casei_166 omitted because of collinearity.
note: _Igwf_casei_167 omitted because of collinearity.
note: _Igwf_casei_169 omitted because of collinearity.
note: _Igwf_casei_170 omitted because of collinearity.
note: _Igwf_casei_171 omitted because of collinearity.
note: _Igwf_casei_173 omitted because of collinearity.
note: _Igwf_casei_175 omitted because of collinearity.
note: _Igwf_casei_176 omitted because of collinearity.
note: _Igwf_casei_177 omitted because of collinearity.
note: _Igwf_casei_178 omitted because of collinearity.
note: _Igwf_casei_179 omitted because of collinearity.
note: _Igwf_casei_182 omitted because of collinearity.
note: _Igwf_casei_183 omitted because of collinearity.
note: _Igwf_casei_184 omitted because of collinearity.
note: _Igwf_casei_187 omitted because of collinearity.
note: _Igwf_casei_188 omitted because of collinearity.
note: _Igwf_casei_189 omitted because of collinearity.
note: _Igwf_casei_190 omitted because of collinearity.
note: _Igwf_casei_191 omitted because of collinearity.
note: _Igwf_casei_192 omitted because of collinearity.
note: _Igwf_casei_193 omitted because of collinearity.
note: _Igwf_casei_196 omitted because of collinearity.
note: _Igwf_casei_198 omitted because of collinearity.
note: _Igwf_casei_199 omitted because of collinearity.
note: _Igwf_casei_200 omitted because of collinearity.
note: _Igwf_casei_201 omitted because of collinearity.
note: _Igwf_casei_203 omitted because of collinearity.
note: _Igwf_casei_204 omitted because of collinearity.
note: _Igwf_casei_206 omitted because of collinearity.
note: _Igwf_casei_207 omitted because of collinearity.
note: _Igwf_casei_211 omitted because of collinearity.
note: _Igwf_casei_213 omitted because of collinearity.
note: _Igwf_casei_215 omitted because of collinearity.
note: _Igwf_casei_216 omitted because of collinearity.
note: _Igwf_casei_217 omitted because of collinearity.
note: _Igwf_casei_218 omitted because of collinearity.
note: _Igwf_casei_219 omitted because of collinearity.
note: _Igwf_casei_221 omitted because of collinearity.
note: _Igwf_casei_224 omitted because of collinearity.
note: _Igwf_casei_226 omitted because of collinearity.
note: _Igwf_casei_227 omitted because of collinearity.
note: _Igwf_casei_228 omitted because of collinearity.
note: _Igwf_casei_229 omitted because of collinearity.
note: _Igwf_casei_232 omitted because of collinearity.
note: _Igwf_casei_233 omitted because of collinearity.
note: _Igwf_casei_234 omitted because of collinearity.
note: _Igwf_casei_236 omitted because of collinearity.
note: _Igwf_casei_237 omitted because of collinearity.
note: _Igwf_casei_240 omitted because of collinearity.
note: _Igwf_casei_241 omitted because of collinearity.
note: _Igwf_casei_242 omitted because of collinearity.
note: _Igwf_casei_243 omitted because of collinearity.
note: _Igwf_casei_244 omitted because of collinearity.
note: _Igwf_casei_245 omitted because of collinearity.
note: _Igwf_casei_248 omitted because of collinearity.
note: _Igwf_casei_249 omitted because of collinearity.
note: _Igwf_casei_250 omitted because of collinearity.
note: _Igwf_casei_253 omitted because of collinearity.
note: _Igwf_casei_254 omitted because of collinearity.
note: _Igwf_casei_258 omitted because of collinearity.
note: _Igwf_casei_259 omitted because of collinearity.
note: _Igwf_casei_261 omitted because of collinearity.
note: _Igwf_casei_265 omitted because of collinearity.
note: _Igwf_casei_266 omitted because of collinearity.
note: _Igwf_casei_267 omitted because of collinearity.
note: _Igwf_casei_269 omitted because of collinearity.
note: _Igwf_casei_270 omitted because of collinearity.
note: _Igwf_casei_271 omitted because of collinearity.
note: _Igwf_casei_272 omitted because of collinearity.
note: _Igwf_casei_273 omitted because of collinearity.
note: _Igwf_casei_275 omitted because of collinearity.
note: _Igwf_casei_276 omitted because of collinearity.
note: _Igwf_casei_277 omitted because of collinearity.
note: _Igwf_casei_280 omitted because of collinearity.
note: _IgwfXtima10 omitted because of collinearity.
note: _IgwfXtima32 omitted because of collinearity.
note: _IgwfXtima120 omitted because of collinearity.
note: _IgwfXtima239 omitted because of collinearity.

Linear regression                               Number of obs     =      4,559
                                                F(23, 503)        =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6475
                                                Root MSE          =     .09683

                          (Std. err. adjusted for 504 clusters in gwf_leaderid)
-------------------------------------------------------------------------------
              |               Robust
         gdem | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
_Igwf_casei_2 |          0  (omitted)
_Igwf_casei_3 |          0  (omitted)
_Igwf_casei_4 |          0  (omitted)
_Igwf_casei_5 |          0  (omitted)
_Igwf_casei_6 |          0  (omitted)
_Igwf_casei_7 |          0  (omitted)
_Igwf_casei_8 |  -26.60541   6.014377    -4.42   0.000    -38.42181   -14.78901
_Igwf_casei_9 |  -29.59481   6.546476    -4.52   0.000    -42.45662   -16.73301
_Igwf_cas~_10 |  -29.39755   6.609624    -4.45   0.000    -42.38342   -16.41168
_Igwf_cas~_11 |   -29.7719   6.665376    -4.47   0.000    -42.86731   -16.67649
_Igwf_cas~_12 |          0  (omitted)
_Igwf_cas~_13 |  -29.41938   6.594725    -4.46   0.000    -42.37598   -16.46278
_Igwf_cas~_14 |          0  (omitted)
_Igwf_cas~_15 |   40.45286   25.00027     1.62   0.106    -8.664954    89.57067
_Igwf_cas~_16 |          0  (omitted)
_Igwf_cas~_17 |          0  (omitted)
_Igwf_cas~_18 |          0  (omitted)
_Igwf_cas~_19 |          0  (omitted)
_Igwf_cas~_20 |          0  (omitted)
_Igwf_cas~_21 |  -23.25058   5.932144    -3.92   0.000    -34.90541   -11.59575
_Igwf_cas~_22 |          0  (omitted)
_Igwf_cas~_23 |          0  (omitted)
_Igwf_cas~_24 |          0  (omitted)
_Igwf_cas~_25 |  -26.99953   6.564881    -4.11   0.000    -39.89749   -14.10156
_Igwf_cas~_26 |          0  (omitted)
_Igwf_cas~_27 |          0  (omitted)
_Igwf_cas~_28 |          0  (omitted)
_Igwf_cas~_29 |          0  (omitted)
_Igwf_cas~_30 |          0  (omitted)
_Igwf_cas~_31 |          0  (omitted)
_Igwf_cas~_32 |  -30.41231   6.534382    -4.65   0.000    -43.25035   -17.57426
_Igwf_cas~_33 |          0  (omitted)
_Igwf_cas~_34 |          0  (omitted)
_Igwf_cas~_35 |  -29.96575   6.619598    -4.53   0.000    -42.97122   -16.96028
_Igwf_cas~_36 |  -26.40282   6.022337    -4.38   0.000    -38.23486   -14.57079
_Igwf_cas~_37 |          0  (omitted)
_Igwf_cas~_38 |   3.392141   17.13377     0.20   0.843    -30.27042    37.05471
_Igwf_cas~_39 |          0  (omitted)
_Igwf_cas~_40 |  -30.04897   6.724983    -4.47   0.000    -43.26149   -16.83646
_Igwf_cas~_41 |  -27.76565   6.656121    -4.17   0.000    -40.84287   -14.68843
_Igwf_cas~_42 |  -30.32178   6.682438    -4.54   0.000     -43.4507   -17.19285
_Igwf_cas~_43 |          0  (omitted)
_Igwf_cas~_44 |  -29.11422    6.50178    -4.48   0.000    -41.88821   -16.34023
_Igwf_cas~_45 |          0  (omitted)
_Igwf_cas~_46 |  -20.58744   7.565181    -2.72   0.007    -35.45069   -5.724197
_Igwf_cas~_47 |  -28.60142   6.401508    -4.47   0.000    -41.17841   -16.02443
_Igwf_cas~_48 |          0  (omitted)
_Igwf_cas~_49 |          0  (omitted)
_Igwf_cas~_50 |          0  (omitted)
_Igwf_cas~_51 |          0  (omitted)
_Igwf_cas~_52 |          0  (omitted)
_Igwf_cas~_53 |          0  (omitted)
_Igwf_cas~_54 |          0  (omitted)
_Igwf_cas~_55 |          0  (omitted)
_Igwf_cas~_56 |          0  (omitted)
_Igwf_cas~_57 |  -29.08902   6.487432    -4.48   0.000    -41.83483   -16.34322
_Igwf_cas~_58 |  -18.05013   5.602737    -3.22   0.001    -29.05778    -7.04248
_Igwf_cas~_59 |          0  (omitted)
_Igwf_cas~_60 |          0  (omitted)
_Igwf_cas~_61 |          0  (omitted)
_Igwf_cas~_62 |          0  (omitted)
_Igwf_cas~_63 |   -29.6767   6.639201    -4.47   0.000    -42.72068   -16.63272
_Igwf_cas~_64 |          0  (omitted)
_Igwf_cas~_65 |   -24.9034   6.079677    -4.10   0.000    -36.84809   -12.95871
_Igwf_cas~_66 |          0  (omitted)
_Igwf_cas~_67 |          0  (omitted)
_Igwf_cas~_68 |          0  (omitted)
_Igwf_cas~_69 |          0  (omitted)
_Igwf_cas~_70 |          0  (omitted)
_Igwf_cas~_71 |  -24.27763   5.803052    -4.18   0.000    -35.67884   -12.87642
_Igwf_cas~_72 |          0  (omitted)
_Igwf_cas~_73 |  -27.46115   6.309323    -4.35   0.000    -39.85703   -15.06528
_Igwf_cas~_74 |          0  (omitted)
_Igwf_cas~_75 |  -30.16137   6.733451    -4.48   0.000    -43.39052   -16.93222
_Igwf_cas~_76 |          0  (omitted)
_Igwf_cas~_77 |          0  (omitted)
_Igwf_cas~_78 |  -29.70883   6.592159    -4.51   0.000    -42.66039   -16.75727
_Igwf_cas~_79 |  -30.17299   6.652502    -4.54   0.000    -43.24311   -17.10288
_Igwf_cas~_80 |          0  (omitted)
_Igwf_cas~_81 |          0  (omitted)
_Igwf_cas~_82 |          0  (omitted)
_Igwf_cas~_83 |  -20.54416   6.512765    -3.15   0.002    -33.33973   -7.748582
_Igwf_cas~_84 |          0  (omitted)
_Igwf_cas~_85 |   148.4012   4.920949    30.16   0.000      138.733    158.0693
_Igwf_cas~_86 |          0  (omitted)
_Igwf_cas~_87 |   23.51296   23.52869     1.00   0.318    -22.71366    69.73958
_Igwf_cas~_88 |  -30.54968   6.557065    -4.66   0.000    -43.43229   -17.66707
_Igwf_cas~_89 |  -30.27403   6.599086    -4.59   0.000     -43.2392   -17.30887
_Igwf_cas~_90 |          0  (omitted)
_Igwf_cas~_91 |   -30.3041   6.595215    -4.59   0.000    -43.26166   -17.34654
_Igwf_cas~_92 |  -3.313455   14.37899    -0.23   0.818    -31.56373    24.93682
_Igwf_cas~_93 |  -27.76523   6.567197    -4.23   0.000    -40.66775   -14.86272
_Igwf_cas~_94 |  -30.59159   6.557621    -4.67   0.000     -43.4753   -17.70789
_Igwf_cas~_95 |          0  (omitted)
_Igwf_cas~_96 |  -27.97302   6.342033    -4.41   0.000    -40.43316   -15.51289
_Igwf_cas~_97 |          0  (omitted)
_Igwf_cas~_98 |          0  (omitted)
_Igwf_cas~_99 |   -26.6103   6.390998    -4.16   0.000    -39.16664   -14.05396
_Igwf_cas~100 |          0  (omitted)
_Igwf_cas~101 |   19.27911   6.207486     3.11   0.002     7.083315     31.4749
_Igwf_cas~102 |          0  (omitted)
_Igwf_cas~103 |  -27.96649   6.250684    -4.47   0.000    -40.24715   -15.68582
_Igwf_cas~104 |   196.3377   10.10726    19.43   0.000     176.4801    216.1953
_Igwf_cas~105 |          0  (omitted)
_Igwf_cas~106 |  -21.47683   6.494552    -3.31   0.001    -34.23662   -8.717036
_Igwf_cas~107 |   6.827118   14.11889     0.48   0.629    -20.91215    34.56638
_Igwf_cas~108 |  -29.92181   6.736746    -4.44   0.000    -43.15744   -16.68619
_Igwf_cas~109 |          0  (omitted)
_Igwf_cas~110 |          0  (omitted)
_Igwf_cas~111 |  -23.83368   6.412501    -3.72   0.000    -36.43227    -11.2351
_Igwf_cas~112 |          0  (omitted)
_Igwf_cas~113 |          0  (omitted)
_Igwf_cas~114 |          0  (omitted)
_Igwf_cas~115 |     -30.08   6.713284    -4.48   0.000    -43.26953   -16.89047
_Igwf_cas~116 |          0  (omitted)
_Igwf_cas~117 |          0  (omitted)
_Igwf_cas~118 |  -24.14067   5.833064    -4.14   0.000    -35.60084    -12.6805
_Igwf_cas~119 |          0  (omitted)
_Igwf_cas~120 |   -29.4005   6.692791    -4.39   0.000    -42.54977   -16.25123
_Igwf_cas~121 |          0  (omitted)
_Igwf_cas~122 |          0  (omitted)
_Igwf_cas~123 |          0  (omitted)
_Igwf_cas~124 |          0  (omitted)
_Igwf_cas~125 |          0  (omitted)
_Igwf_cas~126 |          0  (omitted)
_Igwf_cas~127 |          0  (omitted)
_Igwf_cas~128 |          0  (omitted)
_Igwf_cas~129 |  -1.146902   13.40698    -0.09   0.932    -27.48749    25.19368
_Igwf_cas~130 |  -30.22904   6.567554    -4.60   0.000    -43.13226   -17.32583
_Igwf_cas~131 |          0  (omitted)
_Igwf_cas~132 |  -29.09011     6.4428    -4.52   0.000    -41.74822     -16.432
_Igwf_cas~133 |          0  (omitted)
_Igwf_cas~134 |          0  (omitted)
_Igwf_cas~135 |          0  (omitted)
_Igwf_cas~136 |  -29.61408   6.870246    -4.31   0.000    -43.11199   -16.11616
_Igwf_cas~137 |  -27.82608   6.690363    -4.16   0.000    -40.97058   -14.68158
_Igwf_cas~138 |          0  (omitted)
_Igwf_cas~139 |  -29.06456   6.385894    -4.55   0.000    -41.61087   -16.51825
_Igwf_cas~140 |  -23.88098   6.142343    -3.89   0.000    -35.94879   -11.81318
_Igwf_cas~141 |  -30.11271   6.712731    -4.49   0.000    -43.30115   -16.92426
_Igwf_cas~142 |          0  (omitted)
_Igwf_cas~143 |  -30.40064   6.613207    -4.60   0.000    -43.39355   -17.40773
_Igwf_cas~144 |          0  (omitted)
_Igwf_cas~145 |  -29.59094   6.631133    -4.46   0.000    -42.61907   -16.56281
_Igwf_cas~146 |  -30.34803   6.743885    -4.50   0.000    -43.59768   -17.09838
_Igwf_cas~147 |          0  (omitted)
_Igwf_cas~148 |          0  (omitted)
_Igwf_cas~149 |          0  (omitted)
_Igwf_cas~150 |   1.197656   4.785902     0.25   0.802    -8.205165    10.60048
_Igwf_cas~151 |  -27.05504   6.597675    -4.10   0.000    -40.01744   -14.09265
_Igwf_cas~152 |          0  (omitted)
_Igwf_cas~153 |          0  (omitted)
_Igwf_cas~154 |  -29.65868   6.514036    -4.55   0.000    -42.45675   -16.86061
_Igwf_cas~155 |   -29.0628   6.372854    -4.56   0.000     -41.5835   -16.54211
_Igwf_cas~156 |   89.46592   43.02807     2.08   0.038     4.929045    174.0028
_Igwf_cas~157 |  -30.38832   6.591903    -4.61   0.000    -43.33938   -17.43727
_Igwf_cas~158 |  -26.42091   6.166342    -4.28   0.000    -38.53587   -14.30595
_Igwf_cas~159 |   241.1569   5.176195    46.59   0.000     230.9873    251.3266
_Igwf_cas~160 |  -30.18699   6.587667    -4.58   0.000    -43.12972   -17.24426
_Igwf_cas~161 |  -29.95277   6.659139    -4.50   0.000    -43.03592   -16.86961
_Igwf_cas~162 |          0  (omitted)
_Igwf_cas~163 |  -29.27471   6.540166    -4.48   0.000    -42.12412   -16.42531
_Igwf_cas~164 |  -20.16156    5.69369    -3.54   0.000     -31.3479   -8.975212
_Igwf_cas~165 |  -23.10835   6.427706    -3.60   0.000    -35.73681   -10.47989
_Igwf_cas~166 |  -29.17509    6.59033    -4.43   0.000    -42.12305   -16.22712
_Igwf_cas~167 |  -30.23796   6.541215    -4.62   0.000    -43.08943   -17.38649
_Igwf_cas~168 |          0  (omitted)
_Igwf_cas~169 |  -27.60754   6.543797    -4.22   0.000    -40.46409     -14.751
_Igwf_cas~170 |          0  (omitted)
_Igwf_cas~171 |          0  (omitted)
_Igwf_cas~172 |          0  (omitted)
_Igwf_cas~173 |  -29.46799   6.700343    -4.40   0.000     -42.6321   -16.30389
_Igwf_cas~174 |          0  (omitted)
_Igwf_cas~175 |  -30.48616   6.632357    -4.60   0.000    -43.51669   -17.45562
_Igwf_cas~176 |  -30.27777   6.653446    -4.55   0.000    -43.34973    -17.2058
_Igwf_cas~177 |  -29.62695   6.574007    -4.51   0.000    -42.54284   -16.71106
_Igwf_cas~178 |          0  (omitted)
_Igwf_cas~179 |  -29.88719   6.730453    -4.44   0.000    -43.11045   -16.66392
_Igwf_cas~180 |          0  (omitted)
_Igwf_cas~181 |          0  (omitted)
_Igwf_cas~182 |   848.9621   8.462749   100.32   0.000     832.3354    865.5888
_Igwf_cas~183 |  -30.65151   6.658036    -4.60   0.000     -43.7325   -17.57053
_Igwf_cas~184 |  -30.11476   6.612823    -4.55   0.000    -43.10691    -17.1226
_Igwf_cas~185 |          0  (omitted)
_Igwf_cas~186 |          0  (omitted)
_Igwf_cas~187 |  -29.90532   6.671754    -4.48   0.000    -43.01326   -16.79738
_Igwf_cas~188 |   -22.6332   7.154304    -3.16   0.002     -36.6892   -8.577204
_Igwf_cas~189 |   1351.859   13.28219   101.78   0.000     1325.763    1377.954
_Igwf_cas~190 |  -21.85275   7.172991    -3.05   0.002    -35.94547   -7.760039
_Igwf_cas~191 |  -28.16762   6.930721    -4.06   0.000    -41.78435   -14.55089
_Igwf_cas~192 |   183.9735   60.92311     3.02   0.003     64.27834    303.6686
_Igwf_cas~193 |  -30.65226   6.630737    -4.62   0.000    -43.67962   -17.62491
_Igwf_cas~194 |          0  (omitted)
_Igwf_cas~195 |          0  (omitted)
_Igwf_cas~196 |          0  (omitted)
_Igwf_cas~197 |          0  (omitted)
_Igwf_cas~198 |   75.21997   7.019302    10.72   0.000      61.4292    89.01073
_Igwf_cas~199 |          0  (omitted)
_Igwf_cas~200 |          0  (omitted)
_Igwf_cas~201 |  -29.41283   6.632289    -4.43   0.000    -42.44323   -16.38242
_Igwf_cas~202 |          0  (omitted)
_Igwf_cas~203 |  -30.44611   6.601382    -4.61   0.000    -43.41579   -17.47643
_Igwf_cas~204 |  -30.09418   6.604471    -4.56   0.000    -43.06993   -17.11843
_Igwf_cas~205 |          0  (omitted)
_Igwf_cas~206 |  -28.98754   6.571615    -4.41   0.000    -41.89873   -16.07634
_Igwf_cas~207 |          0  (omitted)
_Igwf_cas~208 |          0  (omitted)
_Igwf_cas~209 |          0  (omitted)
_Igwf_cas~210 |          0  (omitted)
_Igwf_cas~211 |  -30.33658   6.684591    -4.54   0.000    -43.46974   -17.20342
_Igwf_cas~212 |          0  (omitted)
_Igwf_cas~213 |  -30.28525   6.686747    -4.53   0.000    -43.42265   -17.14786
_Igwf_cas~214 |          0  (omitted)
_Igwf_cas~215 |  -29.44655   6.585846    -4.47   0.000    -42.38571    -16.5074
_Igwf_cas~216 |  -29.46437   6.537493    -4.51   0.000    -42.30853   -16.62022
_Igwf_cas~217 |  -26.32324   6.239949    -4.22   0.000    -38.58281   -14.06366
_Igwf_cas~218 |  -30.53542   6.637187    -4.60   0.000    -43.57544   -17.49539
_Igwf_cas~219 |  -29.77961   6.620017    -4.50   0.000     -42.7859   -16.77332
_Igwf_cas~220 |          0  (omitted)
_Igwf_cas~221 |          0  (omitted)
_Igwf_cas~222 |          0  (omitted)
_Igwf_cas~223 |          0  (omitted)
_Igwf_cas~224 |          0  (omitted)
_Igwf_cas~225 |          0  (omitted)
_Igwf_cas~226 |  -29.63791   6.695268    -4.43   0.000    -42.79205   -16.48378
_Igwf_cas~227 |  -30.54937   6.646268    -4.60   0.000    -43.60724    -17.4915
_Igwf_cas~228 |  -29.66193   6.622915    -4.48   0.000    -42.67392   -16.64995
_Igwf_cas~229 |  -29.32891    6.44567    -4.55   0.000    -41.99266   -16.66515
_Igwf_cas~230 |          0  (omitted)
_Igwf_cas~231 |          0  (omitted)
_Igwf_cas~232 |  -30.37433   6.621938    -4.59   0.000     -43.3844   -17.36427
_Igwf_cas~233 |  -19.26116   9.203167    -2.09   0.037    -37.34254   -1.179775
_Igwf_cas~234 |  -9.728896   6.459477    -1.51   0.133    -22.41977    2.961982
_Igwf_cas~235 |          0  (omitted)
_Igwf_cas~236 |          0  (omitted)
_Igwf_cas~237 |  -28.30842   6.434567    -4.40   0.000    -40.95036   -15.66648
_Igwf_cas~238 |          0  (omitted)
_Igwf_cas~239 |          0  (omitted)
_Igwf_cas~240 |          0  (omitted)
_Igwf_cas~241 |  -8.638409   6.819182    -1.27   0.206      -22.036    4.759179
_Igwf_cas~242 |          0  (omitted)
_Igwf_cas~243 |          0  (omitted)
_Igwf_cas~244 |  -30.11823    6.61846    -4.55   0.000    -43.12146     -17.115
_Igwf_cas~245 |   -30.2404   6.682969    -4.52   0.000    -43.37037   -17.11043
_Igwf_cas~246 |          0  (omitted)
_Igwf_cas~247 |          0  (omitted)
_Igwf_cas~248 |          0  (omitted)
_Igwf_cas~249 |  -30.34745   6.748705    -4.50   0.000    -43.60657   -17.08833
_Igwf_cas~250 |  -30.24134   6.502627    -4.65   0.000      -43.017   -17.46569
_Igwf_cas~251 |          0  (omitted)
_Igwf_cas~252 |          0  (omitted)
_Igwf_cas~253 |          0  (omitted)
_Igwf_cas~254 |   93.26247   6.342559    14.70   0.000      80.8013    105.7236
_Igwf_cas~255 |          0  (omitted)
_Igwf_cas~256 |          0  (omitted)
_Igwf_cas~257 |          0  (omitted)
_Igwf_cas~258 |  -27.85108   6.338387    -4.39   0.000    -40.30406   -15.39811
_Igwf_cas~259 |          0  (omitted)
_Igwf_cas~260 |          0  (omitted)
_Igwf_cas~261 |  -28.50564   6.699312    -4.26   0.000    -41.66772   -15.34356
_Igwf_cas~262 |          0  (omitted)
_Igwf_cas~263 |          0  (omitted)
_Igwf_cas~264 |          0  (omitted)
_Igwf_cas~265 |   -21.1274   5.479679    -3.86   0.000    -31.89328   -10.36152
_Igwf_cas~266 |  -28.76631   6.490874    -4.43   0.000    -41.51887   -16.01374
_Igwf_cas~267 |  -11.02376    9.62651    -1.15   0.253    -29.93688    7.889359
_Igwf_cas~268 |          0  (omitted)
_Igwf_cas~269 |  -17.83417   5.827874    -3.06   0.002    -29.28414   -6.384194
_Igwf_cas~270 |  -29.49415   6.813265    -4.33   0.000    -42.88012   -16.10819
_Igwf_cas~271 |  -30.29507   6.599085    -4.59   0.000    -43.26024   -17.32991
_Igwf_cas~272 |  -30.53444   6.758616    -4.52   0.000    -43.81303   -17.25584
_Igwf_cas~273 |  -27.47105   6.538644    -4.20   0.000    -40.31747   -14.62463
_Igwf_cas~274 |          0  (omitted)
_Igwf_cas~275 |  -16.70249   2.975612    -5.61   0.000    -22.54865   -10.85633
_Igwf_cas~276 |  -29.52202   6.581589    -4.49   0.000    -42.45282   -16.59123
_Igwf_cas~277 |  -30.38318    6.53441    -4.65   0.000    -43.22128   -17.54508
_Igwf_cas~278 |          0  (omitted)
_Igwf_cas~279 |          0  (omitted)
_Igwf_cas~280 |   -29.3201   6.681262    -4.39   0.000    -42.44672   -16.19349
         time |  -1.111906   12.97894    -0.09   0.932    -26.61151     24.3877
  _IgwfXtim_2 |   1.107975   23.72924     0.05   0.963    -45.51266    47.72861
  _IgwfXtim_3 |   .7178589   10.79976     0.07   0.947    -20.50033    21.93605
  _IgwfXtim_4 |   .9963706   15.50725     0.06   0.949    -29.47058    31.46332
  _IgwfXtim_5 |   .4426578   19.23113     0.02   0.982    -37.34058     38.2259
  _IgwfXtim_6 |   1.093096   22.29162     0.05   0.961    -42.70306    44.88926
  _IgwfXtim_7 |   1.081649   26.13357     0.04   0.967    -50.26275    52.42604
  _IgwfXtim_8 |   .9815437   25.06733     0.04   0.969    -48.26802    50.23111
  _IgwfXtim_9 |   1.077766   11.32375     0.10   0.924     -21.1699    23.32544
 _IgwfXtim_10 |          0  (omitted)
 _IgwfXtim_11 |   .9888758   10.22906     0.10   0.923    -19.10807    21.08582
 _IgwfXtim_12 |    .807513   30.47659     0.03   0.979    -59.06958    60.68461
 _IgwfXtim_13 |   1.014279   22.22479     0.05   0.964    -42.65058    44.67914
 _IgwfXtim_14 |  -1.817146   26.42404    -0.07   0.945    -53.73223    50.09794
 _IgwfXtim_15 |  -3.147363    18.5345    -0.17   0.865    -39.56193     33.2672
 _IgwfXtim_16 |   -.102981   16.57701    -0.01   0.995    -32.67168    32.46572
 _IgwfXtim_17 |   .9041946   24.65327     0.04   0.971    -47.53187    49.34026
 _IgwfXtim_18 |          0  (omitted)
 _IgwfXtim_19 |   1.029758   20.22914     0.05   0.959    -38.71425    40.77377
 _IgwfXtim_20 |          0  (omitted)
 _IgwfXtim_21 |   .6313663   23.28604     0.03   0.978    -45.11852    46.38125
 _IgwfXtim_22 |   .9624691   20.78078     0.05   0.963    -39.86535    41.79029
 _IgwfXtim_23 |  -2.477703   18.41962    -0.13   0.893    -38.66658    33.71117
 _IgwfXtim_24 |  -.1384791   24.07895    -0.01   0.995    -47.44618    47.16922
 _IgwfXtim_25 |   1.000282   13.52219     0.07   0.941    -25.56665    27.56721
 _IgwfXtim_26 |     1.0263   14.35029     0.07   0.943     -27.1676    29.22019
 _IgwfXtim_27 |  -2.136225   16.79988    -0.13   0.899    -35.14281    30.87036
 _IgwfXtim_28 |  -1.786839   9.451969    -0.19   0.850    -20.35704    16.78336
 _IgwfXtim_29 |  -1.538949   19.26715    -0.08   0.936    -39.39295    36.31505
 _IgwfXtim_30 |          0  (omitted)
 _IgwfXtim_31 |   .7605889    14.6614     0.05   0.959    -28.04453    29.56571
 _IgwfXtim_32 |          0  (omitted)
 _IgwfXtim_33 |   1.027816   21.53139     0.05   0.962    -41.27472    43.33035
 _IgwfXtim_34 |          0  (omitted)
 _IgwfXtim_35 |   1.050361          .        .       .            .           .
 _IgwfXtim_36 |   .7635735   4.725447     0.16   0.872    -8.520472    10.04762
 _IgwfXtim_37 |  -1.325077          .        .       .            .           .
 _IgwfXtim_38 |  -1.246819    21.8901    -0.06   0.955     -44.2541    41.76047
 _IgwfXtim_39 |  -1.514924   13.48057    -0.11   0.911    -28.00009    24.97024
 _IgwfXtim_40 |   1.092402   25.61473     0.04   0.966    -49.23263    51.41743
 _IgwfXtim_41 |   .9152017    24.2296     0.04   0.970    -46.68849     48.5189
 _IgwfXtim_42 |    1.09038   20.38073     0.05   0.957    -38.95147    41.13223
 _IgwfXtim_43 |   .9122528   25.27146     0.04   0.971    -48.73837    50.56287
 _IgwfXtim_44 |   1.026335    23.9351     0.04   0.966    -45.99875    48.05142
 _IgwfXtim_45 |  -.5427021   7.576413    -0.07   0.943    -15.42802    14.34261
 _IgwfXtim_46 |   .6315522   15.78433     0.04   0.968    -30.37979    31.64289
 _IgwfXtim_47 |   1.049803   17.90445     0.06   0.953    -34.12692    36.22653
 _IgwfXtim_48 |   .5974295   14.61546     0.04   0.967    -28.11744     29.3123
 _IgwfXtim_49 |   1.062147   24.97232     0.04   0.966    -48.00076    50.12505
 _IgwfXtim_50 |  -7.465766   14.95814    -0.50   0.618     -36.8539    21.92237
 _IgwfXtim_51 |  -5.636117   17.14995    -0.33   0.743    -39.33047    28.05823
 _IgwfXtim_52 |   1.071881   19.86628     0.05   0.957    -37.95922    40.10299
 _IgwfXtim_53 |   .7219964    22.0929     0.03   0.974    -42.68373    44.12772
 _IgwfXtim_54 |   .4335491   6.693085     0.06   0.948     -12.7163     13.5834
 _IgwfXtim_55 |    1.08527   21.36046     0.05   0.959    -40.88143    43.05197
 _IgwfXtim_56 |   1.079628          .        .       .            .           .
 _IgwfXtim_57 |   1.064522   24.28201     0.04   0.965    -46.64213    48.77118
 _IgwfXtim_58 |   .7301495   12.46337     0.06   0.953    -23.75653    25.21682
 _IgwfXtim_59 |   .8545487   22.26768     0.04   0.969    -42.89457    44.60366
 _IgwfXtim_60 |    1.03128    28.1073     0.04   0.971     -54.1909    56.25346
 _IgwfXtim_61 |  -.5925838   22.08191    -0.03   0.979    -43.97672    42.79155
 _IgwfXtim_62 |  -.1008713   19.65159    -0.01   0.996    -38.71018    38.50844
 _IgwfXtim_63 |   1.051046   18.23646     0.06   0.954    -34.77796    36.88006
 _IgwfXtim_64 |    .566759   4.464529     0.13   0.899    -8.204663    9.338181
 _IgwfXtim_65 |   .8626571          .        .       .            .           .
 _IgwfXtim_66 |   1.042457   17.08466     0.06   0.951    -32.52363    34.60854
 _IgwfXtim_67 |   .6223749   18.19904     0.03   0.973    -35.13312    36.37787
 _IgwfXtim_68 |   1.103907   27.01542     0.04   0.967    -51.97306    54.18088
 _IgwfXtim_69 |    .966161   8.572031     0.11   0.910    -15.87523    17.80756
 _IgwfXtim_70 |  -1.666335   24.41524    -0.07   0.946    -49.63474    46.30207
 _IgwfXtim_71 |    .915544    25.7411     0.04   0.972    -49.65777    51.48886
 _IgwfXtim_72 |   .8292006   13.51868     0.06   0.951    -25.73082    27.38923
 _IgwfXtim_73 |   .8472142   18.48951     0.05   0.963    -35.47896    37.17339
 _IgwfXtim_74 |   .9388904   17.68363     0.05   0.958    -33.80399    35.68177
 _IgwfXtim_75 |   1.095272          .        .       .            .           .
 _IgwfXtim_76 |   .9124352   25.61291     0.04   0.972    -49.40902    51.23389
 _IgwfXtim_77 |          0  (omitted)
 _IgwfXtim_78 |   .9967645   27.45555     0.04   0.971    -52.94493    54.93846
 _IgwfXtim_79 |   1.097431   25.04612     0.04   0.965    -48.11046    50.30533
 _IgwfXtim_80 |   1.081846    22.7272     0.05   0.962    -43.57009    45.73378
 _IgwfXtim_81 |   1.112266   20.63713     0.05   0.957    -39.43333    41.65787
 _IgwfXtim_82 |  -2.120325   19.15037    -0.11   0.912    -39.74489    35.50424
 _IgwfXtim_83 |   .3363796   21.15877     0.02   0.987    -41.23406    41.90682
 _IgwfXtim_84 |          0  (omitted)
 _IgwfXtim_85 |  -18.15512          .        .       .            .           .
 _IgwfXtim_86 |  -1.218297   25.60925    -0.05   0.962    -51.53257    49.09598
 _IgwfXtim_87 |   -2.57202   13.01719    -0.20   0.843    -28.14679    23.00275
 _IgwfXtim_88 |   1.108616   16.24146     0.07   0.946    -30.80085    33.01808
 _IgwfXtim_89 |    1.10021   23.52704     0.05   0.963    -45.12316    47.32358
 _IgwfXtim_90 |   1.106608   23.58759     0.05   0.963    -45.23574    47.44895
 _IgwfXtim_91 |   1.092204   13.61975     0.08   0.936     -25.6664    27.85081
 _IgwfXtim_92 |  -.1921961   23.76135    -0.01   0.994    -46.87591    46.49151
 _IgwfXtim_93 |   1.024659   26.95336     0.04   0.970    -51.93037    53.97969
 _IgwfXtim_94 |   1.110484   10.12497     0.11   0.913    -18.78196    21.00293
 _IgwfXtim_95 |   1.066382   24.74656     0.04   0.966    -47.55296    49.68573
 _IgwfXtim_96 |    1.02954   22.43605     0.05   0.963    -43.05038    45.10946
 _IgwfXtim_97 |   1.098872   16.61519     0.07   0.947    -31.54485     33.7426
 _IgwfXtim_98 |   1.083343   23.42936     0.05   0.963    -44.94812    47.11481
 _IgwfXtim_99 |   .9891021   22.77762     0.04   0.965    -43.76189    45.74009
_IgwfXtim_100 |          0  (omitted)
_IgwfXtim_101 |  -.8331404   22.91919    -0.04   0.971    -45.86228      44.196
_IgwfXtim_102 |   1.076216   21.21399     0.05   0.960    -40.60273    42.75516
_IgwfXtim_103 |   .8492337   15.78937     0.05   0.957    -30.17201    31.87048
_IgwfXtim_104 |  -19.94497   13.93238    -1.43   0.153    -47.31779    7.427855
_IgwfXtim_105 |   -2.49264   12.69203    -0.20   0.844    -27.42857    22.44329
_IgwfXtim_106 |   .7049112   23.14781     0.03   0.976     -44.7734    46.18322
_IgwfXtim_107 |  -1.957659          .        .       .            .           .
_IgwfXtim_108 |   1.077929   14.09651     0.08   0.939    -26.61735    28.77321
_IgwfXtim_109 |   .9301047   20.77115     0.04   0.964     -39.8788    41.73901
_IgwfXtim_110 |   .7836728   16.93186     0.05   0.963    -32.48221    34.04956
_IgwfXtim_111 |   .5624081   21.45632     0.03   0.979    -41.59264    42.71745
_IgwfXtim_112 |   1.034753    19.7824     0.05   0.958    -37.83156    39.90106
_IgwfXtim_113 |  -1.058685   17.39669    -0.06   0.951    -35.23782    33.12045
_IgwfXtim_114 |  -.7494003   20.36063    -0.04   0.971    -40.75176    39.25296
_IgwfXtim_115 |   1.085976   15.29045     0.07   0.943    -28.95503    31.12698
_IgwfXtim_116 |   1.052926   24.32245     0.04   0.965    -46.73317    48.83903
_IgwfXtim_117 |          0  (omitted)
_IgwfXtim_118 |    .738933   10.26487     0.07   0.943    -19.42837    20.90623
_IgwfXtim_119 |   .7319379   19.77787     0.04   0.970    -38.12547    39.58935
_IgwfXtim_120 |          0  (omitted)
_IgwfXtim_121 |   1.012982   16.31953     0.06   0.951    -31.04986    33.07583
_IgwfXtim_122 |   1.088251   19.42192     0.06   0.955    -37.06982    39.24632
_IgwfXtim_123 |  -.2868121   23.91445    -0.01   0.990    -47.27133    46.69771
_IgwfXtim_124 |   -1.21175   1.625944    -0.75   0.456    -4.406228    1.982728
_IgwfXtim_125 |   -47.2734   12.63317    -3.74   0.000    -72.09368   -22.45313
_IgwfXtim_126 |  -15.31992   17.70353    -0.87   0.387    -50.10188    19.46205
_IgwfXtim_127 |   .9829384   15.54139     0.06   0.950    -29.55111    31.51698
_IgwfXtim_128 |   .8976136   25.38095     0.04   0.972    -48.96811    50.76334
_IgwfXtim_129 |  -.8298848    20.0185    -0.04   0.967    -40.16005    38.50028
_IgwfXtim_130 |   1.082612   15.88015     0.07   0.946    -30.11699    32.28222
_IgwfXtim_131 |   1.075753   23.79788     0.05   0.964    -45.67973    47.83124
_IgwfXtim_132 |   1.035251   22.96133     0.05   0.964    -44.07667    46.14717
_IgwfXtim_133 |   1.111894   18.84361     0.06   0.953    -35.90999    38.13378
_IgwfXtim_134 |    1.06926   10.05594     0.11   0.915    -18.68756    20.82608
_IgwfXtim_135 |   1.105875   25.36362     0.04   0.965    -48.72582    50.93756
_IgwfXtim_136 |   1.035225   19.34924     0.05   0.957    -36.98006     39.0505
_IgwfXtim_137 |   .8858472   22.41703     0.04   0.968     -43.1567    44.92839
_IgwfXtim_138 |   1.002589   29.39828     0.03   0.973    -56.75596    58.76114
_IgwfXtim_139 |   1.058591   17.70729     0.06   0.952    -33.73078    35.84796
_IgwfXtim_140 |   .9076328   21.92814     0.04   0.967    -42.17439    43.98965
_IgwfXtim_141 |    1.09154   17.85173     0.06   0.951     -33.9816    36.16468
_IgwfXtim_142 |          0  (omitted)
_IgwfXtim_143 |   1.104369   26.85527     0.04   0.967    -51.65794    53.86668
_IgwfXtim_144 |   1.025124   23.80319     0.04   0.966    -45.74079    47.79104
_IgwfXtim_145 |    1.06017   22.40545     0.05   0.962    -42.95962    45.07996
_IgwfXtim_146 |   1.103253    17.3032     0.06   0.949     -32.8922    35.09871
_IgwfXtim_147 |   1.005805   18.78021     0.05   0.957    -35.89151    37.90312
_IgwfXtim_148 |   .8086051   23.21428     0.03   0.972     -44.8003    46.41751
_IgwfXtim_149 |   1.096788   18.61696     0.06   0.953    -35.47978    37.67336
_IgwfXtim_150 |   .1160258   20.82835     0.01   0.996    -40.80526    41.03731
_IgwfXtim_151 |   .9948106   15.90333     0.06   0.950    -30.25032    32.23994
_IgwfXtim_152 |          0  (omitted)
_IgwfXtim_153 |  -3.700958   17.07631    -0.22   0.829    -37.25065    29.84873
_IgwfXtim_154 |   1.081551    24.5636     0.04   0.965    -47.17834    49.34145
_IgwfXtim_155 |   1.037798   25.21781     0.04   0.967    -48.50742    50.58302
_IgwfXtim_156 |  -4.421024   28.04767    -0.16   0.875    -59.52604    50.68399
_IgwfXtim_157 |   1.099632   24.23248     0.05   0.964    -46.50972    48.70898
_IgwfXtim_158 |    .928456    12.8778     0.07   0.943    -24.37245    26.22936
_IgwfXtim_159 |  -8.970882   27.31987    -0.33   0.743    -62.64599    44.70423
_IgwfXtim_160 |   1.081935   9.304052     0.12   0.907    -17.19766    19.36153
_IgwfXtim_161 |    1.09187   17.72908     0.06   0.951    -33.74029    35.92403
_IgwfXtim_162 |          0  (omitted)
_IgwfXtim_163 |   1.008828    17.0062     0.06   0.953    -32.40311    34.42076
_IgwfXtim_164 |   .4263884   11.74889     0.04   0.971    -22.65655    23.50933
_IgwfXtim_165 |   .7208794   14.04027     0.05   0.959    -26.86393    28.30569
_IgwfXtim_166 |    1.03001          .        .       .            .           .
_IgwfXtim_167 |   1.098721   24.71198     0.04   0.965     -47.4527    49.65014
_IgwfXtim_168 |   .9730006   15.75295     0.06   0.951    -29.97669    31.92269
_IgwfXtim_169 |   .9374992      13.57     0.07   0.945    -25.72336    27.59836
_IgwfXtim_170 |  -.8323108    24.3305    -0.03   0.973    -48.63423    46.96961
_IgwfXtim_171 |   .2109887   21.38272     0.01   0.992    -41.79945    42.22143
_IgwfXtim_172 |   1.059841   8.585233     0.12   0.902    -15.80749    17.92717
_IgwfXtim_173 |   1.073966   22.97835     0.05   0.963    -44.07139    46.21932
_IgwfXtim_174 |   1.102473   28.00547     0.04   0.969    -53.91963    56.12457
_IgwfXtim_175 |   1.097323   12.57332     0.09   0.930    -23.60537    25.80002
_IgwfXtim_176 |   1.101275    19.5034     0.06   0.955     -37.2169    39.41945
_IgwfXtim_177 |   1.079568   28.55249     0.04   0.970    -55.01726     57.1764
_IgwfXtim_178 |  -4.262855   18.21879    -0.23   0.815    -40.05716    31.53145
_IgwfXtim_179 |   1.077916    26.5832     0.04   0.968    -51.14988    53.30571
_IgwfXtim_180 |   1.046617   18.17525     0.06   0.954    -34.66213    36.75537
_IgwfXtim_181 |   1.044522   20.90342     0.05   0.960    -40.02424    42.11328
_IgwfXtim_182 |  -29.21526   20.52787    -1.42   0.155     -69.5462    11.11568
_IgwfXtim_183 |    1.11111   21.23141     0.05   0.958    -40.60206    42.82428
_IgwfXtim_184 |   1.078002   19.80152     0.05   0.957    -37.82587    39.98188
_IgwfXtim_185 |    1.10508   25.85781     0.04   0.966    -49.69754     51.9077
_IgwfXtim_186 |  -.3938347   12.40679    -0.03   0.975    -24.76934    23.98167
_IgwfXtim_187 |    1.07202   21.63052     0.05   0.960    -41.42527    43.56931
_IgwfXtim_188 |   .6777181          .        .       .            .           .
_IgwfXtim_189 |  -52.50636   23.03874    -2.28   0.023    -97.77038   -7.242337
_IgwfXtim_190 |   .4472647    8.84922     0.05   0.960    -16.93872    17.83325
_IgwfXtim_191 |   1.020283   4.230227     0.24   0.810    -7.290808    9.331374
_IgwfXtim_192 |  -7.494686   7.991089    -0.94   0.349    -23.19471    8.205339
_IgwfXtim_193 |   1.113585   29.56841     0.04   0.970    -56.97921    59.20638
_IgwfXtim_194 |   1.068843   23.38396     0.05   0.964    -44.87342    47.01111
_IgwfXtim_195 |    .666881   22.75664     0.03   0.977    -44.04288    45.37665
_IgwfXtim_196 |  -.8301682   3.937858    -0.21   0.833    -8.566843    6.906507
_IgwfXtim_197 |  -.2996098   21.41561    -0.01   0.989    -42.37467    41.77545
_IgwfXtim_198 |  -2.582622   29.87417    -0.09   0.931    -61.27615    56.11091
_IgwfXtim_199 |  -13.26925   22.37994    -0.59   0.554    -57.23892    30.70043
_IgwfXtim_200 |  -6.880227   25.82638    -0.27   0.790    -57.62109    43.86064
_IgwfXtim_201 |   1.053508   16.62979     0.06   0.950     -31.6189    33.72591
_IgwfXtim_202 |  -3.732834   16.27906    -0.23   0.819    -35.71616     28.2505
_IgwfXtim_203 |   1.083486    14.1871     0.08   0.939    -26.78979    28.95676
_IgwfXtim_204 |   1.021293   20.29246     0.05   0.960    -38.84712    40.88971
_IgwfXtim_205 |   1.075705   19.50799     0.06   0.956    -37.25147    39.40288
_IgwfXtim_206 |   .5886574   23.64668     0.02   0.980    -45.86977    47.04708
_IgwfXtim_207 |          0  (omitted)
_IgwfXtim_208 |   .2668598   23.10168     0.01   0.991    -45.12081    45.65453
_IgwfXtim_209 |  -3.249282   24.90569    -0.13   0.896    -52.18128    45.68272
_IgwfXtim_210 |   .4450964   12.53331     0.04   0.972    -24.17899    25.06918
_IgwfXtim_211 |   1.088992   14.35155     0.08   0.940    -27.10738    29.28536
_IgwfXtim_212 |   1.085446   27.15581     0.04   0.968    -52.26735    54.43824
_IgwfXtim_213 |   1.088053   23.03618     0.05   0.962    -44.17094    46.34705
_IgwfXtim_214 |   .9907333   15.13301     0.07   0.948    -28.74095    30.72242
_IgwfXtim_215 |   1.037778   21.28515     0.05   0.961    -40.78096    42.85652
_IgwfXtim_216 |    1.06849   18.27902     0.06   0.953    -34.84414    36.98112
_IgwfXtim_217 |   .9769925   20.08374     0.05   0.961    -38.48136    40.43534
_IgwfXtim_218 |   1.109332   23.43212     0.05   0.962    -44.92755    47.14621
_IgwfXtim_219 |   1.066373   28.88153     0.04   0.971    -55.67692    57.80967
_IgwfXtim_220 |  -2.104271   27.56314    -0.08   0.939    -56.25735     52.0488
_IgwfXtim_221 |          0  (omitted)
_IgwfXtim_222 |   1.079975   14.26326     0.08   0.940    -26.94293    29.10288
_IgwfXtim_223 |  -23.65243   27.27153    -0.87   0.386    -77.23257    29.92771
_IgwfXtim_224 |          0  (omitted)
_IgwfXtim_225 |   1.093531    21.7062     0.05   0.960    -41.55245    43.73951
_IgwfXtim_226 |   1.072575   13.49507     0.08   0.937    -25.44108    27.58623
_IgwfXtim_227 |   1.102622   20.83644     0.05   0.958    -39.83456     42.0398
_IgwfXtim_228 |   1.018058   25.30893     0.04   0.968    -48.70618    50.74229
_IgwfXtim_229 |   1.030635   20.23918     0.05   0.959    -38.73312    40.79439
_IgwfXtim_230 |   1.077799          .        .       .            .           .
_IgwfXtim_231 |   1.095942   21.23135     0.05   0.959    -40.61711    42.80899
_IgwfXtim_232 |   1.083597   22.21587     0.05   0.961    -42.56374    44.73093
_IgwfXtim_233 |   .5440964   24.67843     0.02   0.982     -47.9414     49.0296
_IgwfXtim_234 |  -1.685209          .        .       .            .           .
_IgwfXtim_235 |   1.057777   22.74657     0.05   0.963    -43.63222    45.74777
_IgwfXtim_236 |          0  (omitted)
_IgwfXtim_237 |   1.041347    19.2593     0.05   0.957    -36.79723    38.87993
_IgwfXtim_238 |   1.092998   21.35877     0.05   0.959    -40.87039    43.05639
_IgwfXtim_239 |          0  (omitted)
_IgwfXtim_240 |  -12.48624   14.03861    -0.89   0.374    -40.06777     15.0953
_IgwfXtim_241 |  -5.557696   16.85688    -0.33   0.742    -38.67626    27.56087
_IgwfXtim_242 |          0  (omitted)
_IgwfXtim_243 |          0  (omitted)
_IgwfXtim_244 |   1.096277   17.94528     0.06   0.951    -34.16065    36.35321
_IgwfXtim_245 |   1.090632          .        .       .            .           .
_IgwfXtim_246 |   1.048193   21.36317     0.05   0.961    -40.92384    43.02023
_IgwfXtim_247 |   1.094207   19.39764     0.06   0.955    -37.01618    39.20459
_IgwfXtim_248 |          0  (omitted)
_IgwfXtim_249 |          0  (omitted)
_IgwfXtim_250 |   1.072508   24.63516     0.04   0.965    -47.32798      49.473
_IgwfXtim_251 |   .8393442   16.35482     0.05   0.959    -31.29284    32.97153
_IgwfXtim_252 |   .0422789   20.87107     0.00   0.998    -40.96293    41.04748
_IgwfXtim_253 |          0  (omitted)
_IgwfXtim_254 |  -14.79858   13.30544    -1.11   0.267    -40.93966     11.3425
_IgwfXtim_255 |   1.099771   18.20053     0.06   0.952    -34.65864    36.85819
_IgwfXtim_256 |    1.09962   27.10662     0.04   0.968    -52.15652    54.35576
_IgwfXtim_257 |   .7370291   17.38382     0.04   0.966    -33.41681    34.89086
_IgwfXtim_258 |   .7701849   26.40587     0.03   0.977    -51.10919    52.64956
_IgwfXtim_259 |          0  (omitted)
_IgwfXtim_260 |  -35.86978   18.27198    -1.96   0.050    -71.76858     .029023
_IgwfXtim_261 |   1.049326   20.13534     0.05   0.958     -38.5104    40.60906
_IgwfXtim_262 |   1.088037     22.681     0.05   0.962    -43.47314    45.64921
_IgwfXtim_263 |   .8614732    22.6362     0.04   0.970    -43.61168    45.33463
_IgwfXtim_264 |   .9128737   17.25963     0.05   0.958    -32.99698    34.82273
_IgwfXtim_265 |   .6457869   18.16914     0.04   0.972    -35.05096    36.34253
_IgwfXtim_266 |   1.054465    16.9095     0.06   0.950    -32.16749    34.27642
_IgwfXtim_267 |  -.0977382   13.13176    -0.01   0.994    -25.89759    25.70211
_IgwfXtim_268 |   1.028855   17.52632     0.06   0.953    -33.40496    35.46267
_IgwfXtim_269 |   .7377262   11.73522     0.06   0.950    -22.31836    23.79382
_IgwfXtim_270 |   .8065538   27.11875     0.03   0.976    -52.47342    54.08653
_IgwfXtim_271 |   1.100893   20.41018     0.05   0.957    -38.99881     41.2006
_IgwfXtim_272 |   1.108781   21.45833     0.05   0.959    -41.05022    43.26778
_IgwfXtim_273 |   .8408979   13.57532     0.06   0.951    -25.83042    27.51221
_IgwfXtim_274 |   .9455964   25.71106     0.04   0.971    -49.56869    51.45989
_IgwfXtim_275 |   .2448422   22.32439     0.01   0.991    -43.61569    44.10538
_IgwfXtim_276 |   1.078729   17.36882     0.06   0.951    -33.04564    35.20309
_IgwfXtim_277 |   1.100781   11.63342     0.09   0.925     -21.7553    23.95686
_IgwfXtim_278 |   .9612976   22.16521     0.04   0.965     -42.5865     44.5091
_IgwfXtim_279 |   .9403127   22.72491     0.04   0.967    -43.70713    45.58775
_IgwfXtim_280 |   1.071197    10.7508     0.10   0.921     -20.0508     22.1932
_Igwf_casei_2 |  -30.52086   6.658551    -4.58   0.000    -43.60286   -17.43886
_Igwf_casei_3 |  -23.98444   6.021125    -3.98   0.000     -35.8141   -12.15479
_Igwf_casei_4 |  -27.87788   6.450537    -4.32   0.000    -40.55119   -15.20456
_Igwf_casei_5 |  -11.84358   5.168371    -2.29   0.022    -21.99783   -1.689324
_Igwf_casei_6 |  -30.33003   6.646778    -4.56   0.000     -43.3889   -17.27116
_Igwf_casei_7 |  -29.93524   6.575399    -4.55   0.000    -42.85387   -17.01661
_Igwf_casei_8 |          0  (omitted)
_Igwf_casei_9 |          0  (omitted)
_Igwf_cas~_10 |          0  (omitted)
_Igwf_cas~_11 |          0  (omitted)
_Igwf_cas~_12 |  -28.39141   6.467072    -4.39   0.000    -41.09721   -15.68561
_Igwf_cas~_13 |          0  (omitted)
_Igwf_cas~_14 |   3.690316   15.32214     0.24   0.810    -26.41295    33.79359
_Igwf_cas~_15 |          0  (omitted)
_Igwf_cas~_16 |   1.271522   10.61849     0.12   0.905    -19.59054    22.13358
_Igwf_cas~_17 |  -23.94024   6.555327    -3.65   0.000    -36.81944   -11.06105
_Igwf_cas~_18 |          0  (omitted)
_Igwf_cas~_19 |  -27.73466   6.656001    -4.17   0.000    -40.81165   -14.65767
_Igwf_cas~_20 |          0  (omitted)
_Igwf_cas~_21 |          0  (omitted)
_Igwf_cas~_22 |  -27.52136   6.983827    -3.94   0.000    -41.24242   -13.80029
_Igwf_cas~_23 |   40.93106   6.206002     6.60   0.000     28.73818    53.12394
_Igwf_cas~_24 |          0  (omitted)
_Igwf_cas~_25 |          0  (omitted)
_Igwf_cas~_26 |  -29.38786   7.527557    -3.90   0.000    -44.17718   -14.59853
_Igwf_cas~_27 |          0  (omitted)
_Igwf_cas~_28 |          0  (omitted)
_Igwf_cas~_29 |          0  (omitted)
_Igwf_cas~_30 |          0  (omitted)
_Igwf_cas~_31 |  -24.41199   6.567526    -3.72   0.000    -37.31516   -11.50883
_Igwf_cas~_32 |          0  (omitted)
_Igwf_cas~_33 |  -30.23306   6.487998    -4.66   0.000    -42.97998   -17.48615
_Igwf_cas~_34 |          0  (omitted)
_Igwf_cas~_35 |          0  (omitted)
_Igwf_cas~_36 |          0  (omitted)
_Igwf_cas~_37 |          0  (omitted)
_Igwf_cas~_38 |          0  (omitted)
_Igwf_cas~_39 |          0  (omitted)
_Igwf_cas~_40 |          0  (omitted)
_Igwf_cas~_41 |          0  (omitted)
_Igwf_cas~_42 |          0  (omitted)
_Igwf_cas~_43 |   -28.4253   6.486104    -4.38   0.000    -41.16849    -15.6821
_Igwf_cas~_44 |          0  (omitted)
_Igwf_cas~_45 |          0  (omitted)
_Igwf_cas~_46 |          0  (omitted)
_Igwf_cas~_47 |          0  (omitted)
_Igwf_cas~_48 |   -25.3886   6.023279    -4.22   0.000    -37.22249   -13.55472
_Igwf_cas~_49 |  -29.53842    6.64162    -4.45   0.000    -42.58715   -16.48969
_Igwf_cas~_50 |   157.7835   4.749102    33.22   0.000      148.453     167.114
_Igwf_cas~_51 |   149.5149   5.463491    27.37   0.000     138.7808    160.2489
_Igwf_cas~_52 |  -30.06034   6.517164    -4.61   0.000    -42.86456   -17.25613
_Igwf_cas~_53 |  -24.67236   6.137366    -4.02   0.000    -36.73039   -12.61433
_Igwf_cas~_54 |  -19.10392   6.070734    -3.15   0.002    -31.03104   -7.176801
_Igwf_cas~_55 |  -29.68789    6.66094    -4.46   0.000    -42.77458    -16.6012
_Igwf_cas~_56 |  -29.95893   6.679384    -4.49   0.000    -43.08186     -16.836
_Igwf_cas~_57 |          0  (omitted)
_Igwf_cas~_58 |          0  (omitted)
_Igwf_cas~_59 |   -27.9362   6.433125    -4.34   0.000     -40.5753   -15.29709
_Igwf_cas~_60 |  -29.17554   6.408221    -4.55   0.000    -41.76571   -16.58536
_Igwf_cas~_61 |          0  (omitted)
_Igwf_cas~_62 |  -5.784313   6.440888    -0.90   0.370    -18.43867    6.870045
_Igwf_cas~_63 |          0  (omitted)
_Igwf_cas~_64 |  -21.20682   5.690271    -3.73   0.000    -32.38644   -10.02719
_Igwf_cas~_65 |          0  (omitted)
_Igwf_cas~_66 |  -28.30152   6.279138    -4.51   0.000    -40.63809   -15.96495
_Igwf_cas~_67 |  -21.98576   6.509278    -3.38   0.001    -34.77448   -9.197033
_Igwf_cas~_68 |   -30.3694   6.639934    -4.57   0.000    -43.41482   -17.32398
_Igwf_cas~_69 |  -29.88212   6.645667    -4.50   0.000    -42.93881   -16.82544
_Igwf_cas~_70 |  -17.40654    6.87714    -2.53   0.012      -30.918   -3.895079
_Igwf_cas~_71 |          0  (omitted)
_Igwf_cas~_72 |  -27.78297   6.679635    -4.16   0.000    -40.90639   -14.65955
_Igwf_cas~_73 |          0  (omitted)
_Igwf_cas~_74 |  -27.62302    6.58717    -4.19   0.000    -40.56477   -14.68126
_Igwf_cas~_75 |          0  (omitted)
_Igwf_cas~_76 |  -24.19419   5.922066    -4.09   0.000    -35.82922   -12.55915
_Igwf_cas~_77 |          0  (omitted)
_Igwf_cas~_78 |          0  (omitted)
_Igwf_cas~_79 |          0  (omitted)
_Igwf_cas~_80 |  -30.20655   6.709815    -4.50   0.000    -43.38927   -17.02384
_Igwf_cas~_81 |  -30.49854   6.669838    -4.57   0.000    -43.60271   -17.39436
_Igwf_cas~_82 |          0  (omitted)
_Igwf_cas~_83 |          0  (omitted)
_Igwf_cas~_84 |  -30.36525   6.657611    -4.56   0.000     -43.4454    -17.2851
_Igwf_cas~_85 |          0  (omitted)
_Igwf_cas~_86 |          0  (omitted)
_Igwf_cas~_87 |          0  (omitted)
_Igwf_cas~_88 |          0  (omitted)
_Igwf_cas~_89 |          0  (omitted)
_Igwf_cas~_90 |  -30.52577   6.677512    -4.57   0.000    -43.64503   -17.40652
_Igwf_cas~_91 |          0  (omitted)
_Igwf_cas~_92 |          0  (omitted)
_Igwf_cas~_93 |          0  (omitted)
_Igwf_cas~_94 |          0  (omitted)
_Igwf_cas~_95 |  -29.39528   6.784867    -4.33   0.000    -42.72545   -16.06511
_Igwf_cas~_96 |          0  (omitted)
_Igwf_cas~_97 |  -30.21621   6.615508    -4.57   0.000    -43.21364   -17.21878
_Igwf_cas~_98 |  -29.92051   6.561297    -4.56   0.000    -42.81143   -17.02959
_Igwf_cas~_99 |          0  (omitted)
_Igwf_cas~100 |          0  (omitted)
_Igwf_cas~101 |          0  (omitted)
_Igwf_cas~102 |  -30.10828   6.732078    -4.47   0.000    -43.33474   -16.88183
_Igwf_cas~103 |          0  (omitted)
_Igwf_cas~104 |          0  (omitted)
_Igwf_cas~105 |   22.32181   19.37705     1.15   0.250     -15.7481    60.39173
_Igwf_cas~106 |          0  (omitted)
_Igwf_cas~107 |          0  (omitted)
_Igwf_cas~108 |          0  (omitted)
_Igwf_cas~109 |  -28.80331   6.747822    -4.27   0.000     -42.0607   -15.54592
_Igwf_cas~110 |  -26.86656   6.786241    -3.96   0.000    -40.19943   -13.53369
_Igwf_cas~111 |          0  (omitted)
_Igwf_cas~112 |  -29.05988    6.62327    -4.39   0.000    -42.07256    -16.0472
_Igwf_cas~113 |   16.91141   17.06181     0.99   0.322    -16.60979     50.4326
_Igwf_cas~114 |          0  (omitted)
_Igwf_cas~115 |          0  (omitted)
_Igwf_cas~116 |  -28.77129   6.580432    -4.37   0.000    -41.69981   -15.84278
_Igwf_cas~117 |          0  (omitted)
_Igwf_cas~118 |          0  (omitted)
_Igwf_cas~119 |  -22.31801   6.669946    -3.35   0.001    -35.42239   -9.213621
_Igwf_cas~120 |          0  (omitted)
_Igwf_cas~121 |  -29.93173   6.568574    -4.56   0.000    -42.83695   -17.02651
_Igwf_cas~122 |  -30.08209   6.469663    -4.65   0.000    -42.79299    -17.3712
_Igwf_cas~123 |          0  (omitted)
_Igwf_cas~124 |          0  (omitted)
_Igwf_cas~125 |   1095.962   6.832779   160.40   0.000     1082.538    1109.387
_Igwf_cas~126 |   424.7999   104.8948     4.05   0.000     218.7139    630.8859
_Igwf_cas~127 |  -30.39439     6.6759    -4.55   0.000    -43.51047   -17.27831
_Igwf_cas~128 |  -27.92377   6.315362    -4.42   0.000    -40.33151   -15.51604
_Igwf_cas~129 |          0  (omitted)
_Igwf_cas~130 |          0  (omitted)
_Igwf_cas~131 |  -30.21251   6.598465    -4.58   0.000    -43.17646   -17.24857
_Igwf_cas~132 |          0  (omitted)
_Igwf_cas~133 |  -30.55121   6.637953    -4.60   0.000    -43.59274   -17.50968
_Igwf_cas~134 |  -29.28095   6.502913    -4.50   0.000    -42.05717   -16.50473
_Igwf_cas~135 |  -30.51118   6.612907    -4.61   0.000    -43.50351   -17.51886
_Igwf_cas~136 |          0  (omitted)
_Igwf_cas~137 |          0  (omitted)
_Igwf_cas~138 |  -28.64396   6.344648    -4.51   0.000    -41.10924   -16.17869
_Igwf_cas~139 |          0  (omitted)
_Igwf_cas~140 |          0  (omitted)
_Igwf_cas~141 |          0  (omitted)
_Igwf_cas~142 |          0  (omitted)
_Igwf_cas~143 |          0  (omitted)
_Igwf_cas~144 |  -27.81318   6.527625    -4.26   0.000    -40.63795   -14.98842
_Igwf_cas~145 |          0  (omitted)
_Igwf_cas~146 |          0  (omitted)
_Igwf_cas~147 |  -29.04603   6.574149    -4.42   0.000     -41.9622   -16.12985
_Igwf_cas~148 |  -29.30252   6.702303    -4.37   0.000    -42.47047   -16.13456
_Igwf_cas~149 |  -30.15603   6.517748    -4.63   0.000    -42.96139   -17.35066
_Igwf_cas~150 |          0  (omitted)
_Igwf_cas~151 |          0  (omitted)
_Igwf_cas~152 |          0  (omitted)
_Igwf_cas~153 |          0  (omitted)
_Igwf_cas~154 |          0  (omitted)
_Igwf_cas~155 |          0  (omitted)
_Igwf_cas~156 |          0  (omitted)
_Igwf_cas~157 |          0  (omitted)
_Igwf_cas~158 |          0  (omitted)
_Igwf_cas~159 |          0  (omitted)
_Igwf_cas~160 |          0  (omitted)
_Igwf_cas~161 |          0  (omitted)
_Igwf_cas~162 |          0  (omitted)
_Igwf_cas~163 |          0  (omitted)
_Igwf_cas~164 |          0  (omitted)
_Igwf_cas~165 |          0  (omitted)
_Igwf_cas~166 |          0  (omitted)
_Igwf_cas~167 |          0  (omitted)
_Igwf_cas~168 |  -28.92955   6.469648    -4.47   0.000    -41.64041   -16.21868
_Igwf_cas~169 |          0  (omitted)
_Igwf_cas~170 |          0  (omitted)
_Igwf_cas~171 |          0  (omitted)
_Igwf_cas~172 |  -29.74669   6.551601    -4.54   0.000    -42.61857   -16.87482
_Igwf_cas~173 |          0  (omitted)
_Igwf_cas~174 |  -30.54789    6.75402    -4.52   0.000    -43.81745   -17.27832
_Igwf_cas~175 |          0  (omitted)
_Igwf_cas~176 |          0  (omitted)
_Igwf_cas~177 |          0  (omitted)
_Igwf_cas~178 |          0  (omitted)
_Igwf_cas~179 |          0  (omitted)
_Igwf_cas~180 |  -28.57662   6.570866    -4.35   0.000    -41.48634   -15.66689
_Igwf_cas~181 |  -28.38288   6.321939    -4.49   0.000    -40.80354   -15.96222
_Igwf_cas~182 |          0  (omitted)
_Igwf_cas~183 |          0  (omitted)
_Igwf_cas~184 |          0  (omitted)
_Igwf_cas~185 |  -30.46706   6.633772    -4.59   0.000    -43.50037   -17.43374
_Igwf_cas~186 |  -1.706598   6.246033    -0.27   0.785    -13.97813    10.56493
_Igwf_cas~187 |          0  (omitted)
_Igwf_cas~188 |          0  (omitted)
_Igwf_cas~189 |          0  (omitted)
_Igwf_cas~190 |          0  (omitted)
_Igwf_cas~191 |          0  (omitted)
_Igwf_cas~192 |          0  (omitted)
_Igwf_cas~193 |          0  (omitted)
_Igwf_cas~194 |  -30.25151   6.680342    -4.53   0.000    -43.37632    -17.1267
_Igwf_cas~195 |  -26.47199   6.835497    -3.87   0.000    -39.90163   -13.04234
_Igwf_cas~196 |          0  (omitted)
_Igwf_cas~197 |  -4.992021   6.170922    -0.81   0.419    -17.11598    7.131937
_Igwf_cas~198 |          0  (omitted)
_Igwf_cas~199 |          0  (omitted)
_Igwf_cas~200 |          0  (omitted)
_Igwf_cas~201 |          0  (omitted)
_Igwf_cas~202 |   64.68615   20.09685     3.22   0.001     25.20204    104.1703
_Igwf_cas~203 |          0  (omitted)
_Igwf_cas~204 |          0  (omitted)
_Igwf_cas~205 |  -30.00362   6.675566    -4.49   0.000    -43.11905   -16.88819
_Igwf_cas~206 |          0  (omitted)
_Igwf_cas~207 |          0  (omitted)
_Igwf_cas~208 |  -18.81814   8.150397    -2.31   0.021    -34.83115   -2.805124
_Igwf_cas~209 |   78.06501   5.801579    13.46   0.000      66.6667    89.46332
_Igwf_cas~210 |  -19.60209   6.433098    -3.05   0.002    -32.24114   -6.963032
_Igwf_cas~211 |          0  (omitted)
_Igwf_cas~212 |  -30.44259   6.726197    -4.53   0.000    -43.65749   -17.22769
_Igwf_cas~213 |          0  (omitted)
_Igwf_cas~214 |  -26.77806   6.195357    -4.32   0.000    -38.95003    -14.6061
_Igwf_cas~215 |          0  (omitted)
_Igwf_cas~216 |          0  (omitted)
_Igwf_cas~217 |          0  (omitted)
_Igwf_cas~218 |          0  (omitted)
_Igwf_cas~219 |          0  (omitted)
_Igwf_cas~220 |    48.6955   6.339686     7.68   0.000     36.23997    61.15103
_Igwf_cas~221 |          0  (omitted)
_Igwf_cas~222 |  -29.75448   6.454671    -4.61   0.000    -42.43592   -17.07305
_Igwf_cas~223 |   563.5913   5.073669   111.08   0.000     553.6231    573.5595
_Igwf_cas~224 |          0  (omitted)
_Igwf_cas~225 |  -30.07012   6.551235    -4.59   0.000    -42.94127   -17.19896
_Igwf_cas~226 |          0  (omitted)
_Igwf_cas~227 |          0  (omitted)
_Igwf_cas~228 |          0  (omitted)
_Igwf_cas~229 |          0  (omitted)
_Igwf_cas~230 |  -29.77353   6.571513    -4.53   0.000    -42.68452   -16.86253
_Igwf_cas~231 |  -30.48258   6.714301    -4.54   0.000     -43.6741   -17.29105
_Igwf_cas~232 |          0  (omitted)
_Igwf_cas~233 |          0  (omitted)
_Igwf_cas~234 |          0  (omitted)
_Igwf_cas~235 |  -29.41812    6.79904    -4.33   0.000    -42.77613    -16.0601
_Igwf_cas~236 |          0  (omitted)
_Igwf_cas~237 |          0  (omitted)
_Igwf_cas~238 |  -30.01577   6.761223    -4.44   0.000    -43.29948   -16.73205
_Igwf_cas~239 |  -28.21434   16.45724    -1.71   0.087    -60.54774    4.119065
_Igwf_cas~240 |          0  (omitted)
_Igwf_cas~241 |          0  (omitted)
_Igwf_cas~242 |          0  (omitted)
_Igwf_cas~243 |          0  (omitted)
_Igwf_cas~244 |          0  (omitted)
_Igwf_cas~245 |          0  (omitted)
_Igwf_cas~246 |  -28.44737   6.700347    -4.25   0.000    -41.61149   -15.28326
_Igwf_cas~247 |  -30.09984   6.605844    -4.56   0.000    -43.07829    -17.1214
_Igwf_cas~248 |          0  (omitted)
_Igwf_cas~249 |          0  (omitted)
_Igwf_cas~250 |          0  (omitted)
_Igwf_cas~251 |  -27.96982   6.569331    -4.26   0.000    -40.87653   -15.06311
_Igwf_cas~252 |  -11.43343   7.618695    -1.50   0.134    -26.40182    3.534955
_Igwf_cas~253 |          0  (omitted)
_Igwf_cas~254 |          0  (omitted)
_Igwf_cas~255 |  -30.21206   6.431619    -4.70   0.000    -42.84821   -17.57592
_Igwf_cas~256 |  -30.27812   6.572458    -4.61   0.000    -43.19097   -17.36526
_Igwf_cas~257 |  -30.48036   6.626241    -4.60   0.000    -43.49888   -17.46185
_Igwf_cas~258 |          0  (omitted)
_Igwf_cas~259 |          0  (omitted)
_Igwf_cas~260 |   627.0781   8.328378    75.29   0.000     610.7154    643.4408
_Igwf_cas~261 |          0  (omitted)
_Igwf_cas~262 |  -29.86724   6.701399    -4.46   0.000    -43.03342   -16.70106
_Igwf_cas~263 |  -27.14934   6.168688    -4.40   0.000    -39.26891   -15.02977
_Igwf_cas~264 |  -27.13092   6.395636    -4.24   0.000    -39.69637   -14.56547
_Igwf_cas~265 |          0  (omitted)
_Igwf_cas~266 |          0  (omitted)
_Igwf_cas~267 |          0  (omitted)
_Igwf_cas~268 |  -27.92158   6.449207    -4.33   0.000    -40.59228   -15.25088
_Igwf_cas~269 |          0  (omitted)
_Igwf_cas~270 |          0  (omitted)
_Igwf_cas~271 |          0  (omitted)
_Igwf_cas~272 |          0  (omitted)
_Igwf_cas~273 |          0  (omitted)
_Igwf_cas~274 |  -28.03422   6.500929    -4.31   0.000    -40.80653    -15.2619
_Igwf_cas~275 |          0  (omitted)
_Igwf_cas~276 |          0  (omitted)
_Igwf_cas~277 |          0  (omitted)
_Igwf_cas~278 |  -28.06256   6.553267    -4.28   0.000    -40.93771   -15.18742
_Igwf_cas~279 |  -25.05513   5.891603    -4.25   0.000    -36.63031   -13.47995
_Igwf_cas~280 |          0  (omitted)
        time2 |   .0099759   .0303379     0.33   0.742    -.0496287    .0695805
  _IgwfXtima2 |   -.009935   .1189153    -0.08   0.933    -.2435668    .2236969
  _IgwfXtima3 |  -.0039563   .3579712    -0.01   0.991    -.7072591    .6993466
  _IgwfXtima4 |   -.008693   .2452087    -0.04   0.972    -.4904524    .4730663
  _IgwfXtima5 |  -.0039563   .4382958    -0.01   0.993    -.8650722    .8571597
  _IgwfXtima6 |  -.0095968   .2580107    -0.04   0.970    -.5165082    .4973145
  _IgwfXtima7 |  -.0096098   .2494436    -0.04   0.969    -.4996895    .4804699
  _IgwfXtima8 |  -.0088912   .1407084    -0.06   0.950    -.2853398    .2675575
  _IgwfXtima9 |  -.0096774          .        .       .            .           .
 _IgwfXtima10 |          0  (omitted)
 _IgwfXtima11 |  -.0040279   .3528671    -0.01   0.991     -.697303    .6892471
 _IgwfXtima12 |    .001484   .4106586     0.00   0.997    -.8053334    .8083015
 _IgwfXtima13 |  -.0077089   .3905723    -0.02   0.984     -.775063    .7596451
 _IgwfXtima14 |   .0526947   .1722754     0.31   0.760    -.2857733    .3911626
 _IgwfXtima15 |   .0538926   .3593312     0.15   0.881    -.6520823    .7598675
 _IgwfXtima16 |   .0016647    .407887     0.00   0.997    -.7997073    .8030368
 _IgwfXtima17 |  -.0083304   .3216625    -0.03   0.979    -.6402979    .6236372
 _IgwfXtima18 |   .0003374   .0795719     0.00   0.997    -.1559969    .1566717
 _IgwfXtima19 |  -.0093791   .2712958    -0.03   0.972    -.5423916    .5236333
 _IgwfXtima20 |   .0003348   .0080958     0.04   0.967     -.015571    .0162405
 _IgwfXtima21 |     -.0019   .3490425    -0.01   0.996    -.6876608    .6838609
 _IgwfXtima22 |  -.0080653   .2930036    -0.03   0.978    -.5837271    .5675964
 _IgwfXtima23 |   .0351026   .2799021     0.13   0.900    -.5148187    .5850239
 _IgwfXtima24 |   .0028752   .3138222     0.01   0.993    -.6136886     .619439
 _IgwfXtima25 |  -.0090877   .3586524    -0.03   0.980    -.7137291    .6955537
 _IgwfXtima26 |  -.0083607   .3008654    -0.03   0.978    -.5994684    .5827471
 _IgwfXtima27 |   .0769071   .1493876     0.51   0.607    -.2165934    .3704076
 _IgwfXtima28 |   .0591707   .4247093     0.14   0.889    -.7752519    .8935933
 _IgwfXtima29 |   .0479111   .3096846     0.15   0.877    -.5605236    .6563458
 _IgwfXtima30 |  -.0145171   .5734807    -0.03   0.980     -1.14123    1.112195
 _IgwfXtima31 |  -.0049265   .2069854    -0.02   0.981    -.4115889     .401736
 _IgwfXtima32 |          0  (omitted)
 _IgwfXtima33 |  -.0014552   .2181696    -0.01   0.995    -.4300912    .4271807
 _IgwfXtima34 |  -.6670132   2.447087    -0.27   0.785    -5.474783    4.140757
 _IgwfXtima35 |  -.0081485   .3902543    -0.02   0.983    -.7748778    .7585807
 _IgwfXtima36 |  -.0024554   .3232197    -0.01   0.994    -.6374823    .6325716
 _IgwfXtima37 |   .0389327   .3419519     0.11   0.909    -.6328972    .7107625
 _IgwfXtima38 |   .0310538   .0571334     0.54   0.587    -.0811957    .1433032
 _IgwfXtima39 |   .0403284   .3858827     0.10   0.917    -.7178121    .7984689
 _IgwfXtima40 |  -.0098074   .2423153    -0.04   0.968    -.4858821    .4662673
 _IgwfXtima41 |  -.0065283          .        .       .            .           .
 _IgwfXtima42 |  -.0095287   .4260407    -0.02   0.982    -.8465673    .8275098
 _IgwfXtima43 |  -.0050428   .3228059    -0.02   0.988    -.6392568    .6291712
 _IgwfXtima44 |  -.0086721   .3651972    -0.02   0.981     -.726172    .7088278
 _IgwfXtima45 |   .0125507   .1954367     0.06   0.949    -.3714221    .3965234
 _IgwfXtima46 |  -.0040914   .1552964    -0.03   0.979    -.3092008     .301018
 _IgwfXtima47 |   -.009474   .3154002    -0.03   0.976    -.6291381    .6101901
 _IgwfXtima48 |    .003263   .3332709     0.01   0.992    -.6515115    .6580375
 _IgwfXtima49 |  -.0093361   .3640841    -0.03   0.980     -.724649    .7059767
 _IgwfXtima50 |   .0876965   .2018555     0.43   0.664    -.3088873    .4842804
 _IgwfXtima51 |   .0532598   .3892738     0.14   0.891    -.7115431    .8180627
 _IgwfXtima52 |  -.0090704   .1453315    -0.06   0.950     -.294602    .2764612
 _IgwfXtima53 |  -.0033483    .123451    -0.03   0.978    -.2458913    .2391948
 _IgwfXtima54 |    .000228    .317711     0.00   0.999    -.6239761    .6244321
 _IgwfXtima55 |  -.0097701   .2553492    -0.04   0.969    -.5114526    .4919123
 _IgwfXtima56 |  -.0095189   .3148912    -0.03   0.976     -.628183    .6091451
 _IgwfXtima57 |  -.0095867    .273069    -0.04   0.972    -.5460831    .5269097
 _IgwfXtima58 |   -.007034   .1640767    -0.04   0.966    -.3293941    .3153262
 _IgwfXtima59 |  -.0033483   .3138768    -0.01   0.991    -.6200192    .6133227
 _IgwfXtima60 |   -.008713          .        .       .            .           .
 _IgwfXtima61 |   .0139599    .186425     0.07   0.940    -.3523078    .3802276
 _IgwfXtima62 |   .0048997   .3262412     0.02   0.988    -.6360637     .645863
 _IgwfXtima63 |   -.008827   .3275563    -0.03   0.979     -.652374      .63472
 _IgwfXtima64 |     -.0019   .4501421    -0.00   0.997    -.8862904    .8824904
 _IgwfXtima65 |  -.0071682   .1514349    -0.05   0.962    -.3046911    .2903547
 _IgwfXtima66 |  -.0094297    .281591    -0.03   0.973     -.562669    .5438097
 _IgwfXtima67 |  -.0029509   .3887694    -0.01   0.994    -.7667628     .760861
 _IgwfXtima68 |  -.0099156   .3961067    -0.03   0.980    -.7881431    .7683119
 _IgwfXtima69 |    .000228          .        .       .            .           .
 _IgwfXtima70 |   .1371716    .310305     0.44   0.659     -.472482    .7468252
 _IgwfXtima71 |  -.0084234   .3200992    -0.03   0.979    -.6373195    .6204727
 _IgwfXtima72 |   -.002388   .3316948    -0.01   0.994    -.6540658    .6492899
 _IgwfXtima73 |   -.004064   .4695495    -0.01   0.993    -.9265838    .9184559
 _IgwfXtima74 |  -.0073998   .2710619    -0.03   0.978    -.5399527    .5251532
 _IgwfXtima75 |  -.0097987   .2152203    -0.05   0.964    -.4326402    .4130428
 _IgwfXtima76 |   -.008395          .        .       .            .           .
 _IgwfXtima77 |  -2.896211   4.442372    -0.65   0.515     -11.6241     5.83168
 _IgwfXtima78 |  -.0052942   .1735509    -0.03   0.976    -.3462682    .3356797
 _IgwfXtima79 |  -.0098471   .4537712    -0.02   0.983    -.9013674    .8816732
 _IgwfXtima80 |  -.0093724   .4051296    -0.02   0.982    -.8053271    .7865822
 _IgwfXtima81 |   -.010239   .3364877    -0.03   0.976    -.6713335    .6508555
 _IgwfXtima82 |   .0760693   .2449395     0.31   0.756    -.4051613    .5572999
 _IgwfXtima83 |   .0050881   .3880996     0.01   0.990    -.7574078     .767584
 _IgwfXtima84 |   2.069627   12.75831     0.16   0.871    -22.99652    27.13578
 _IgwfXtima85 |   .5088632   .3188622     1.60   0.111    -.1176026    1.135329
 _IgwfXtima86 |   .0347498   .1765962     0.20   0.844    -.3122072    .3817069
 _IgwfXtima87 |    .052778   .3369541     0.16   0.876    -.6092328    .7147888
 _IgwfXtima88 |  -.0099209   .2997022    -0.03   0.974    -.5987433    .5789015
 _IgwfXtima89 |  -.0098751   .2382226    -0.04   0.967    -.4779089    .4581587
 _IgwfXtima90 |    -.00982   .2692559    -0.04   0.971    -.5388247    .5191848
 _IgwfXtima91 |  -.0096258   .4027269    -0.02   0.981    -.8008599    .7816083
 _IgwfXtima92 |   .0056251   .2504424     0.02   0.982    -.4864169    .4976672
 _IgwfXtima93 |  -.0092783   .3170411    -0.03   0.977    -.6321663    .6136097
 _IgwfXtima94 |  -.0099681   .2454969    -0.04   0.968    -.4922938    .4723576
 _IgwfXtima95 |  -.0095066   .4812673    -0.02   0.984    -.9550482    .9360351
 _IgwfXtima96 |  -.0093061   .4712938    -0.02   0.984     -.935253    .9166408
 _IgwfXtima97 |  -.0098654   .3115757    -0.03   0.975    -.6220155    .6022847
 _IgwfXtima98 |  -.0096694     .20469    -0.05   0.962    -.4118221    .3924833
 _IgwfXtima99 |   -.009002   .1229204    -0.07   0.942    -.2505027    .2324986
_IgwfXtima100 |  -.0001352   .0440429    -0.00   0.998    -.0866659    .0863955
_IgwfXtima101 |   .0090049   .2569364     0.04   0.972    -.4957959    .5138057
_IgwfXtima102 |  -.0093038   .3647787    -0.03   0.980    -.7259813    .7073737
_IgwfXtima103 |  -.0029324   .2676239    -0.01   0.991    -.5287307     .522866
_IgwfXtima104 |   .4788078   .3150843     1.52   0.129    -.1402356    1.097851
_IgwfXtima105 |   .0514826   .4681323     0.11   0.912    -.8682529     .971218
_IgwfXtima106 |  -.0054008   .1575724    -0.03   0.973    -.3149819    .3041802
_IgwfXtima107 |    .053082   .3211576     0.17   0.869    -.5778935    .6840575
_IgwfXtima108 |  -.0107579   .3367948    -0.03   0.975    -.6724557    .6509399
_IgwfXtima109 |   -.004816   .3118765    -0.02   0.988    -.6175571    .6079251
_IgwfXtima110 |   -.002388   .3349651    -0.01   0.994     -.660491    .6557151
_IgwfXtima111 |   .0014728   .3534143     0.00   0.997    -.6928772    .6958227
_IgwfXtima112 |  -.0089506    .403971    -0.02   0.982     -.802629    .7847278
_IgwfXtima113 |   .0148348   .1745216     0.09   0.932    -.3280462    .3577159
_IgwfXtima114 |   .0119401   .0718569     0.17   0.868    -.1292365    .1531168
_IgwfXtima115 |  -.0095933   .1522734    -0.06   0.950    -.3087636     .289577
_IgwfXtima116 |  -.0094752          .        .       .            .           .
_IgwfXtima117 |   .0005157   .0308249     0.02   0.987    -.0600456    .0610771
_IgwfXtima118 |  -.0044363   .3311044    -0.01   0.989    -.6549543    .6460817
_IgwfXtima119 |  -.0055838   .3186167    -0.02   0.986    -.6315672    .6203996
_IgwfXtima120 |          0  (omitted)
_IgwfXtima121 |  -.0051526   .4161598    -0.01   0.990    -.8227783     .812473
_IgwfXtima122 |  -.0096668   .2939516    -0.03   0.974     -.587191    .5678575
_IgwfXtima123 |   .0061082   .4427001     0.01   0.989    -.8636609    .8758773
_IgwfXtima124 |   .0276901   .1768082     0.16   0.876    -.3196835    .3750638
_IgwfXtima125 |    .509611   .2534517     2.01   0.045     .0116567    1.007565
_IgwfXtima126 |    .138268   .3416884     0.40   0.686    -.5330444    .8095803
_IgwfXtima127 |   .0072359   .3504094     0.02   0.984    -.6812104    .6956822
_IgwfXtima128 |  -.0054645          .        .       .            .           .
_IgwfXtima129 |   .0220849   .1264308     0.17   0.861    -.2263126    .2704823
_IgwfXtima130 |  -.0094032   .1756707    -0.05   0.957    -.3545419    .3357356
_IgwfXtima131 |  -.0089346   .3529464    -0.03   0.980    -.7023655    .6844962
_IgwfXtima132 |  -.0089608   .1839914    -0.05   0.961    -.3704471    .3525255
_IgwfXtima133 |  -.0100141   .1031693    -0.10   0.923      -.21271    .1926818
_IgwfXtima134 |  -.0096183    .436347    -0.02   0.982    -.8669055    .8476689
_IgwfXtima135 |  -.0098164   .4799169    -0.02   0.984     -.952705    .9330723
_IgwfXtima136 |  -.0081412   .2619708    -0.03   0.975     -.522833    .5065507
_IgwfXtima137 |  -.0050187   .2528748    -0.02   0.984    -.5018396    .4918023
_IgwfXtima138 |  -.0083235   .1151629    -0.07   0.942    -.2345831     .217936
_IgwfXtima139 |  -.0094866   .3291112    -0.03   0.977    -.6560886    .6371153
_IgwfXtima140 |  -.0083896   .4106543    -0.02   0.984    -.8151986    .7984195
_IgwfXtima141 |   -.009757          .        .       .            .           .
_IgwfXtima142 |   .0002253   .0643695     0.00   0.997    -.1262408    .1266915
_IgwfXtima143 |  -.0099111     .29531    -0.03   0.973    -.5901042    .5702819
_IgwfXtima144 |  -.0092829   .1570057    -0.06   0.953    -.3177507    .2991849
_IgwfXtima145 |  -.0092965   .1418623    -0.07   0.948    -.2880121    .2694191
_IgwfXtima146 |  -.0099032   .1943363    -0.05   0.959     -.391714    .3719076
_IgwfXtima147 |  -.0081001   .2856996    -0.03   0.977    -.5694116    .5532114
_IgwfXtima148 |   .0086474   .2482405     0.03   0.972    -.4790686    .4963635
_IgwfXtima149 |  -.0098448   .2354782    -0.04   0.967    -.4724869    .4527972
_IgwfXtima150 |  -.0021301   .3683111    -0.01   0.995    -.7257478    .7214876
_IgwfXtima151 |   -.008984   .4433727    -0.02   0.984    -.8800746    .8621066
_IgwfXtima152 |  -.0850723   .2380943    -0.36   0.721    -.5528541    .3827096
_IgwfXtima153 |   .1761897          .        .       .            .           .
_IgwfXtima154 |  -.0097192   .3892528    -0.02   0.980    -.7744808    .7550424
_IgwfXtima155 |  -.0090288   .3835379    -0.02   0.981    -.7625624    .7445049
_IgwfXtima156 |   .0537989   .2936239     0.18   0.855    -.5230814    .6306793
_IgwfXtima157 |  -.0097639   .3181057    -0.03   0.976    -.6347435    .6152158
_IgwfXtima158 |  -.0078829   .4565513    -0.02   0.986    -.9048653    .8890996
_IgwfXtima159 |   .0835766   .2180977     0.38   0.702    -.3449181    .5120713
_IgwfXtima160 |  -.0092712   .3129196    -0.03   0.976    -.6240616    .6055192
_IgwfXtima161 |  -.0098084   .3781414    -0.03   0.979    -.7527396    .7331227
_IgwfXtima162 |   1.86e-06   9.78e-07     1.90   0.058    -6.32e-08    3.78e-06
_IgwfXtima163 |  -.0077943    .292566    -0.03   0.979    -.5825961    .5670075
_IgwfXtima164 |    .001484   .2621314     0.01   0.995    -.5135233    .5164913
_IgwfXtima165 |  -.0048332   .3991256    -0.01   0.990    -.7889919    .7793254
_IgwfXtima166 |  -.0087549   .3312233    -0.03   0.979    -.6595065    .6419966
_IgwfXtima167 |  -.0098583   .2163915    -0.05   0.964    -.4350009    .4152843
_IgwfXtima168 |  -.0068318   .3524708    -0.02   0.985    -.6993282    .6856646
_IgwfXtima169 |  -.0073777   .3040153    -0.02   0.981    -.6046738    .5899185
_IgwfXtima170 |   .0140116   .0684462     0.20   0.838    -.1204641    .1484873
_IgwfXtima171 |   -.003308   .1635717    -0.02   0.984     -.324676    .3180599
_IgwfXtima172 |  -.0091008   .3837393    -0.02   0.981    -.7630301    .7448285
_IgwfXtima173 |  -.0096329   .2725699    -0.04   0.972    -.5451487    .5258828
_IgwfXtima174 |  -.0097932   .4444974    -0.02   0.982    -.8830934     .863507
_IgwfXtima175 |  -.0096651   .1452627    -0.07   0.947    -.2950614    .2757313
_IgwfXtima176 |  -.0098931   .5276505    -0.02   0.985    -1.046563    1.026777
_IgwfXtima177 |  -.0097023   .4177229    -0.02   0.981    -.8303989    .8109944
_IgwfXtima178 |   .2240811   .2967995     0.75   0.451    -.3590384    .8072005
_IgwfXtima179 |  -.0095206   .3199559    -0.03   0.976    -.6381352    .6190939
_IgwfXtima180 |  -.0094271          .        .       .            .           .
_IgwfXtima181 |  -.0094501   .2568074    -0.04   0.971    -.5139974    .4950973
_IgwfXtima182 |   .2514724   .3468019     0.73   0.469    -.4298862    .9328311
_IgwfXtima183 |  -.0099721   .0420886    -0.24   0.813    -.0926632     .072719
_IgwfXtima184 |  -.0093443   .2222856    -0.04   0.966    -.4460669    .4273783
_IgwfXtima185 |  -.0098711    .091756    -0.11   0.914    -.1901433    .1704012
_IgwfXtima186 |   .0096848   .3479708     0.03   0.978    -.6739705    .6933402
_IgwfXtima187 |  -.0092916   .4138956    -0.02   0.982    -.8224687    .8038854
_IgwfXtima188 |  -.0040084   .3569052    -0.01   0.991     -.705217    .6972001
_IgwfXtima189 |   .5099713   .4107275     1.24   0.215    -.2969815    1.316924
_IgwfXtima190 |    .002709   .3581274     0.01   0.994    -.7009008    .7063189
_IgwfXtima191 |  -.0090651   .3034782    -0.03   0.976    -.6053062    .5871759
_IgwfXtima192 |   .0763706   .3174467     0.24   0.810    -.5473142    .7000555
_IgwfXtima193 |  -.0100057   .3097272    -0.03   0.974    -.6185241    .5985126
_IgwfXtima194 |  -.0081129   .2832914    -0.03   0.977    -.5646931    .5484673
_IgwfXtima195 |   .0021905    .237905     0.01   0.993    -.4652194    .4696004
_IgwfXtima196 |   .0210956          .        .       .            .           .
_IgwfXtima197 |   .0095109   .3773333     0.03   0.980    -.7318327    .7508544
_IgwfXtima198 |    .022289   .3141651     0.07   0.943    -.5949484    .6395265
_IgwfXtima199 |   1.692905   .4772995     3.55   0.000     .7551591    2.630651
_IgwfXtima200 |   .5157247   .2468608     2.09   0.037     .0307194     1.00073
_IgwfXtima201 |  -.0091985          .        .       .            .           .
_IgwfXtima202 |    .051655   .3807346     0.14   0.892    -.6963709    .7996809
_IgwfXtima203 |   .0061628   .2717339     0.02   0.982    -.5277104    .5400361
_IgwfXtima204 |  -.0043004   .4133174    -0.01   0.992    -.8163416    .8077408
_IgwfXtima205 |  -.0093661   .3167548    -0.03   0.976    -.6316916    .6129594
_IgwfXtima206 |   .0350051   .1495477     0.23   0.815    -.2588099    .3288201
_IgwfXtima207 |    -.04601   1.397963    -0.03   0.974    -2.792576    2.700556
_IgwfXtima208 |   .0052473   .1380419     0.04   0.970    -.2659625     .276457
_IgwfXtima209 |   .0338137   .4533599     0.07   0.941    -.8568987    .9245261
_IgwfXtima210 |   .0001962   .1964559     0.00   0.999    -.3857791    .3861714
_IgwfXtima211 |  -.0094766   .3127141    -0.03   0.976    -.6238632      .60491
_IgwfXtima212 |  -.0089078   .4565921    -0.02   0.984    -.9059704    .8881549
_IgwfXtima213 |  -.0094653   .2323128    -0.04   0.968    -.4658883    .4469576
_IgwfXtima214 |   -.008998   .1948144    -0.05   0.963    -.3917482    .3737523
_IgwfXtima215 |  -.0086523   .1483951    -0.06   0.954    -.3002028    .2828983
_IgwfXtima216 |   -.009526    .276388    -0.03   0.973    -.5525431    .5334912
_IgwfXtima217 |  -.0088846   .2747277    -0.03   0.974    -.5486398    .5308705
_IgwfXtima218 |  -.0099644   .2520376    -0.04   0.968    -.5051405    .4852117
_IgwfXtima219 |  -.0093439   .2453564    -0.04   0.970    -.4913934    .4727056
_IgwfXtima220 |   .0226661   .3182234     0.07   0.943    -.6025448    .6478769
_IgwfXtima221 |  -.0220111   .5698951    -0.04   0.969    -1.141679    1.097657
_IgwfXtima222 |  -.0096567   .2532548    -0.04   0.970    -.5072242    .4879108
_IgwfXtima223 |   .2481001   .2811367     0.88   0.378    -.3042468    .8004469
_IgwfXtima224 |   .0005812          .        .       .            .           .
_IgwfXtima225 |   -.009817   .3614042    -0.03   0.978    -.7198647    .7002306
_IgwfXtima226 |  -.0095298   .1421585    -0.07   0.947    -.2888273    .2697677
_IgwfXtima227 |  -.0097649   .1868551    -0.05   0.958    -.3768774    .3573477
_IgwfXtima228 |  -.0071467   .2416525    -0.03   0.976    -.4819192    .4676258
_IgwfXtima229 |  -.0085497   .3160081    -0.03   0.978    -.6294081    .6123087
_IgwfXtima230 |  -.0095881   .4047446    -0.02   0.981    -.8047864    .7856102
_IgwfXtima231 |  -.0096189   .2328021    -0.04   0.967    -.4670033    .4477654
_IgwfXtima232 |  -.0089763   .3175559    -0.03   0.977    -.6328757    .6149232
_IgwfXtima233 |  -.0028422   .3042119    -0.01   0.993    -.6005248    .5948403
_IgwfXtima234 |   .0839811   .2716453     0.31   0.757    -.4497181    .6176803
_IgwfXtima235 |  -.0092862   .3197772    -0.03   0.977    -.6375497    .6189772
_IgwfXtima236 |  -.0005009          .        .       .            .           .
_IgwfXtima237 |  -.0094117          .        .       .            .           .
_IgwfXtima238 |  -.0098174          .        .       .            .           .
_IgwfXtima239 |          0  (omitted)
_IgwfXtima240 |   1.494793   .4931261     3.03   0.003     .5259522    2.463634
_IgwfXtima241 |   .4997448   .3027313     1.65   0.099    -.0950288    1.094518
_IgwfXtima242 |  -.1275412          .        .       .            .           .
_IgwfXtima243 |   -.049387   .5463738    -0.09   0.928    -1.122843    1.024069
_IgwfXtima244 |  -.0098452   .2821303    -0.03   0.972    -.5641442    .5444538
_IgwfXtima245 |  -.0096569   .1275536    -0.08   0.940    -.2602604    .2409466
_IgwfXtima246 |  -.0094837          .        .       .            .           .
_IgwfXtima247 |  -.0098225   .2417921    -0.04   0.968    -.4848693    .4652243
_IgwfXtima248 |   .0003774   .0132038     0.03   0.977    -.0255639    .0263188
_IgwfXtima249 |   1.069627          .        .       .            .           .
_IgwfXtima250 |  -.0082488   .2798518    -0.03   0.976    -.5580712    .5415735
_IgwfXtima251 |  -.0027625   .3362057    -0.01   0.993    -.6633029    .6577778
_IgwfXtima252 |   .0049913   .3112577     0.02   0.987    -.6065341    .6165166
_IgwfXtima253 |   .0003484   .1632146     0.00   0.998     -.320318    .3210148
_IgwfXtima254 |    .501484    .108895     4.61   0.000      .287539    .7154291
_IgwfXtima255 |  -.0098748   .3839271    -0.03   0.979     -.764173    .7444234
_IgwfXtima256 |  -.0098659   .3796403    -0.03   0.979     -.755742    .7360102
_IgwfXtima257 |   .1329452   .2419693     0.55   0.583    -.3424497    .6083401
_IgwfXtima258 |    .001484    .355185     0.00   0.997     -.696345     .699313
_IgwfXtima259 |  -.0660365   .8827162    -0.07   0.940    -1.800302    1.668229
_IgwfXtima260 |   .5099952   .2558032     1.99   0.047      .007421     1.01257
_IgwfXtima261 |  -.0094868   .2712918    -0.03   0.972    -.5424916    .5235179
_IgwfXtima262 |  -.0097747   .2313841    -0.04   0.966    -.4643731    .4448237
_IgwfXtima263 |  -.0051722   .4328032    -0.01   0.990     -.855497    .8451525
_IgwfXtima264 |  -.0069656   .3488733    -0.02   0.984    -.6923939    .6784628
_IgwfXtima265 |  -.0041243   .2741474    -0.02   0.988    -.5427393    .5344906
_IgwfXtima266 |  -.0095097   .1237604    -0.08   0.939    -.2526607    .2336413
_IgwfXtima267 |   .0087723   .3291215     0.03   0.979    -.6378499    .6553945
_IgwfXtima268 |  -.0093149   .4137201    -0.02   0.982    -.8221473    .8035175
_IgwfXtima269 |  -.0072028   .2486615    -0.03   0.977    -.4957458    .4813402
_IgwfXtima270 |   .0130672   .2296927     0.06   0.955     -.438208    .4643424
_IgwfXtima271 |  -.0098816   .3847206    -0.03   0.980    -.7657388    .7459756
_IgwfXtima272 |  -.0099192   .3426889    -0.03   0.977     -.683197    .6633587
_IgwfXtima273 |  -.0037791   .2854174    -0.01   0.989    -.5645362     .556978
_IgwfXtima274 |  -.0071049      .3688    -0.02   0.985     -.731683    .7174733
_IgwfXtima275 |   .0037533   .2657636     0.01   0.989    -.5183902    .5258969
_IgwfXtima276 |  -.0097037          .        .       .            .           .
_IgwfXtima277 |  -.0098396   .0901594    -0.11   0.913     -.186975    .1672959
_IgwfXtima278 |  -.0076994   .4166768    -0.02   0.985    -.8263407     .810942
_IgwfXtima279 |  -.0086316   .3521335    -0.02   0.980    -.7004654    .6832021
_IgwfXtima280 |  -.0096388   .4129473    -0.02   0.981    -.8209528    .8016752
           ld |   .0796706   .0116089     6.86   0.000     .0568628    .1024785
     lnregion |   .0055098   .0024993     2.20   0.028     .0005994    .0104203
       xpers2 |  -.0169883   .0089409    -1.90   0.058    -.0345544    .0005778
        _cons |   30.29354   6.666648     4.54   0.000     17.19563    43.39144
-------------------------------------------------------------------------------

.         est store dem6

.         
.         estout dem1 dem2 dem3 dem4 dem5 using Table2.tex, cells(b(star  fmt(%9
> .4f)) se(par fmt(%9.3f))) ///
>                 stats(r2 N N_clust) style(tex) replace label starlevels(* 0.05
> ) title(\label{tab2})
(file Table2.tex not found)
(output written to Table2.tex)

.  
.         * Interactive fixed effects, within: m_ are panel unit means; y_ are y
> ear means; and mXy_ are their interaction *
.         meprobit gdem ld lnregion  xpers2 ///
>                 m_ld m_lnregion m_xpers2 y_ld y_lnregion y_xpers2  ///
>                 mXy_ld mXy_lnregion mXy_xpers2 ||  ///
>                 gwf_caseid: ,vce(cluster gwf_caseid)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -604.52591  
Iteration 1:   log likelihood = -351.30424  
Iteration 2:   log likelihood = -313.70836  
Iteration 3:   log likelihood =  -309.9608  
Iteration 4:   log likelihood = -309.92654  
Iteration 5:   log likelihood = -309.92652  

Refining starting values:

Grid node 0:   log likelihood = -333.36273

Fitting full model:

Iteration 0:   log pseudolikelihood = -333.36273  (not concave)
Iteration 1:   log pseudolikelihood = -317.92528  (not concave)
Iteration 2:   log pseudolikelihood = -311.59693  (not concave)
Iteration 3:   log pseudolikelihood = -310.99659  (not concave)
Iteration 4:   log pseudolikelihood = -310.76121  (not concave)
Iteration 5:   log pseudolikelihood =  -310.6679  (not concave)
Iteration 6:   log pseudolikelihood =  -310.5938  (not concave)
Iteration 7:   log pseudolikelihood =  -310.5901  (not concave)
Iteration 8:   log pseudolikelihood = -310.58714  (not concave)
Iteration 9:   log pseudolikelihood = -310.58706  (not concave)
Iteration 10:  log pseudolikelihood = -310.58703  (not concave)
Iteration 11:  log pseudolikelihood = -310.58702  (not concave)
Iteration 12:  log pseudolikelihood = -310.58701  (not concave)
Iteration 13:  log pseudolikelihood = -310.58701  (not concave)
Iteration 14:  log pseudolikelihood = -310.58701  (not concave)
Iteration 15:  log pseudolikelihood = -310.58701  (not concave)
Iteration 16:  log pseudolikelihood = -310.58701  (not concave)
Iteration 17:  log pseudolikelihood = -310.58701  (not concave)
Iteration 18:  log pseudolikelihood = -310.58701  (not concave)
Iteration 19:  log pseudolikelihood = -310.58701  (backed up)
Iteration 20:  log pseudolikelihood = -310.58701  (not concave)
Iteration 21:  log pseudolikelihood = -310.58701  (backed up)
Iteration 22:  log pseudolikelihood = -310.58701  (backed up)
Iteration 23:  log pseudolikelihood = -310.58701  (not concave)
Iteration 24:  log pseudolikelihood = -310.58701  (backed up)
Iteration 25:  log pseudolikelihood = -310.58701  (not concave)
Iteration 26:  log pseudolikelihood = -310.58701  (backed up)
Iteration 27:  log pseudolikelihood = -310.58701  (not concave)
Iteration 28:  log pseudolikelihood = -310.58701  (backed up)
Iteration 29:  log pseudolikelihood = -310.58701  (not concave)
Iteration 30:  log pseudolikelihood = -310.58701  (backed up)
Iteration 31:  log pseudolikelihood = -310.58701  (not concave)
Iteration 32:  log pseudolikelihood = -310.58701  (backed up)
Iteration 33:  log pseudolikelihood = -310.58701  (not concave)
Iteration 34:  log pseudolikelihood = -310.58701  (backed up)
Iteration 35:  log pseudolikelihood =   -310.587  (not concave)
Iteration 36:  log pseudolikelihood =   -310.587  (not concave)
Iteration 37:  log pseudolikelihood =   -310.587  
Iteration 38:  log pseudolikelihood = -310.58698  (backed up)
Iteration 39:  log pseudolikelihood = -310.58694  (backed up)
Iteration 40:  log pseudolikelihood = -310.58679  (not concave)
Iteration 41:  log pseudolikelihood = -310.58672  (not concave)
Iteration 42:  log pseudolikelihood = -310.58652  
Iteration 43:  log pseudolikelihood = -310.57655  (backed up)
Iteration 44:  log pseudolikelihood = -310.55698  (not concave)
Iteration 45:  log pseudolikelihood = -310.55502  (not concave)
Iteration 46:  log pseudolikelihood =  -310.5519  (not concave)
Iteration 47:  log pseudolikelihood =  -310.5494  (not concave)
Iteration 48:  log pseudolikelihood = -310.53353  
Iteration 49:  log pseudolikelihood = -310.49723  
Iteration 50:  log pseudolikelihood = -309.92765  
Iteration 51:  log pseudolikelihood = -309.92652  (not concave)
Iteration 52:  log pseudolikelihood = -309.92652  (backed up)

Mixed-effects probit regression                 Number of obs     =      4,559
Group variable: gwf_caseid                      Number of groups  =        280

                                                Obs per group:
                                                              min =          1
                                                              avg =       16.3
                                                              max =         65

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(12)     =     147.91
Log pseudolikelihood = -309.92652               Prob > chi2       =     0.0000
                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
        gdem | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
          ld |   1.984175   .2637835     7.52   0.000     1.467169    2.501182
    lnregion |   .1263739   .0645941     1.96   0.050    -.0002281     .252976
      xpers2 |  -.7543844   .1358141    -5.55   0.000    -1.020575   -.4881937
        m_ld |  -1.337516   .7892187    -1.69   0.090    -2.884356    .2093247
  m_lnregion |  -.1197416   .1708473    -0.70   0.483     -.454596    .2151129
    m_xpers2 |   .5493919   .1558897     3.52   0.000     .2438537      .85493
        y_ld |   .5867028   .6574376     0.89   0.372    -.7018512    1.875257
  y_lnregion |   .2659514   .1352837     1.97   0.049     .0008003    .5311026
    y_xpers2 |   .3997872   .8895112     0.45   0.653    -1.343623    2.143197
      mXy_ld |  -.6341231     .28339    -2.24   0.025    -1.189557   -.0786889
mXy_lnregion |  -.5531728   .2190869    -2.52   0.012    -.9825753   -.1237704
  mXy_xpers2 |  -.1076218   .4441592    -0.24   0.809    -.9781578    .7629143
       _cons |  -2.413799   1.675178    -1.44   0.150    -5.697087    .8694887
-------------+----------------------------------------------------------------
gwf_caseid   |
   var(_cons)|   1.52e-33   9.53e-34                      4.47e-34    5.19e-33
------------------------------------------------------------------------------

. 
.          * Check with IRT-2PL instead of generalized SFP *
.          reghdfe gdem ld lnregion irtpers,a(gwf_caseid year)cluster(gwf_caseid
> )
(dropped 24 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =      4,535
Absorbing 2 HDFE groups                           F(   3,    255) =      11.47
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1799
                                                  Adj R-squared   =     0.1170
                                                  Within R-sq.    =     0.0155
Number of clusters (gwf_caseid) =        256      Root MSE        =     0.1304

                           (Std. err. adjusted for 256 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
        gdem | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
          ld |   .0327774   .0058862     5.57   0.000     .0211857     .044369
    lnregion |   .0034515   .0033225     1.04   0.300    -.0030917    .0099946
    irtpers2 |  -.0843711   .0225988    -3.73   0.000    -.1288752   -.0398671
       _cons |  -.0229356   .0121865    -1.88   0.061    -.0469346    .0010633
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  gwf_caseid |       256         256           0    *|
        year |        65           0          65     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.          reghdfe gdem l1v2x_pol l2v2x_pol ld lnregion irtpers,a(gwf_caseid yea
> r)cluster(gwf_caseid)
(dropped 24 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =      4,519
Absorbing 2 HDFE groups                           F(   5,    255) =      11.88
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1883
                                                  Adj R-squared   =     0.1254
                                                  Within R-sq.    =     0.0254
Number of clusters (gwf_caseid) =        256      Root MSE        =     0.1300

                            (Std. err. adjusted for 256 clusters in gwf_caseid)
-------------------------------------------------------------------------------
              |               Robust
         gdem | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
l1v2x_polya~y |   .3671493   .0894889     4.10   0.000     .1909178    .5433807
l2v2x_polya~y |  -.1709656    .077223    -2.21   0.028    -.3230417   -.0188895
           ld |   .0310926   .0058454     5.32   0.000     .0195811    .0426041
     lnregion |   .0033192   .0033264     1.00   0.319    -.0032316    .0098699
     irtpers2 |  -.0786166   .0229536    -3.43   0.001    -.1238194   -.0334138
        _cons |  -.0606095   .0169747    -3.57   0.000     -.094038   -.0271809
-------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  gwf_caseid |       256         256           0    *|
        year |        65           0          65     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. 
.         * Kernel estimator *
.         krls gdem ld lnregion coldwar xpers2 m_ld m_lnregion m_coldwar m_xpers
> 2, ///
>                 d(k)lambda(2.4)ltolerance(4.5)sigma(8)

Pointwise Derivatives                                    Number of obs =     455
> 9 
                                                         Lambda        =      2.
> 4 
                                                         Tolerance     =        
> 0 
                                                         Sigma         =        
> 8 
                                                         Eff. df       =    125.
> 7 
                                                         R2            =    .226
> 6 
                                                         Looloss       =      59
> 1

       gdem |      Avg.       SE        t    P>|t|        P25       P50       P7
> 5       
------------+-------------------------------------------------------------------
> -
         ld |  .020585   .003674    5.602    0.000    .001967   .013992   .03179
> 6  
   lnregion |  -.00551   .003263   -1.688    0.091   -.018996   -.00816   .00438
> 2  
   *coldwar | -.071066   .011807   -6.019    0.000   -.128262  -.060857  -.00478
> 1  
     xpers2 | -.018477   .003904   -4.733    0.000   -.030505  -.014112  -.00137
> 4  
       m_ld | -.058094   .005008  -11.600    0.000   -.104108    -.0484  -.00392
> 1  
 m_lnregion |  .003983   .009716    0.410    0.682   -.021213   .003756   .02888
> 3  
  m_coldwar |   .08077   .013656    5.915    0.000   -.004013   .035266   .13411
> 9  
   m_xpers2 |  .014092   .004503    3.129    0.002   -.000268   .010847   .02603
> 7  
------------+-------------------------------------------------------------------
> -


.         replace k_xpers2=k_xpers2*100
(4,559 real changes made)

.         *use temp-krls,clear
.         twoway lpolyci k_xpers2 year if year>=1949, yline(0,lcol(red)lpat(dash
> ))legend(off)ylab(-2(1)0) ///
>                 xlab(1950(10)2010)xtit(Year) ytit(Marginal effect of Security 
> personalism) ///
>                 tit(Security personalism and democratic transition) ///
>                 note("Kernel regression, within transformation to proxy for re
> gime-case fixed effects",pos(6)size(vsmall))

.         graph export "$dir\krls-dem.pdf", as(pdf) replace  
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\krls-dem.pdf saved as PDF format

.         save temp-krls,replace
(file temp-krls.dta not found)
file temp-krls.dta saved

.         
.          
.                 
.                 
.         *** Other ways of modeling the time trend ***
.         use temp-fe,clear

.         forval i =1/12{
  2.                 egen m_period`i'=mean(period`i'),by(gwf_caseid)
  3.         }

.         qui reghdfe gdem ld lnregion coldwar xpers2,a(gwf_caseid)cluster(gwf_c
> aseid)

.         est store timedem1

.         lincom xpers2

 ( 1)  xpers2 = 0

------------------------------------------------------------------------------
        gdem | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0208168   .0062761    -3.32   0.001    -.0331764   -.0084572
------------------------------------------------------------------------------

.         qui reghdfe gdem ld lnregion d19* d20 xpers2,a(gwf_caseid)cluster(gwf_
> caseid)

.         est store timedem2

.         lincom xpers2

 ( 1)  xpers2 = 0

------------------------------------------------------------------------------
        gdem | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0226875   .0064111    -3.54   0.000     -.035313    -.010062
------------------------------------------------------------------------------

.         qui reghdfe gdem ld lnregion period* xpers2,a(gwf_caseid)cluster(gwf_c
> aseid)

.         est store timedem3

.         lincom xpers2

 ( 1)  xpers2 = 0

------------------------------------------------------------------------------
        gdem | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0240392   .0065177    -3.69   0.000    -.0368745   -.0112038
------------------------------------------------------------------------------

.         qui reghdfe gdem ld lnregion time xpers2,a(gwf_caseid)cluster(gwf_case
> id)

.         est store timedem4

.         lincom xpers2

 ( 1)  xpers2 = 0

------------------------------------------------------------------------------
        gdem | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0237351    .006563    -3.62   0.000    -.0366597   -.0108106
------------------------------------------------------------------------------

.         qui reghdfe gdem ld lnregion time time2 xpers2,a(gwf_caseid)cluster(gw
> f_caseid)

.         est store timedem5

.         lincom xpers2

 ( 1)  xpers2 = 0

------------------------------------------------------------------------------
        gdem | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0238893   .0065889    -3.63   0.000    -.0368648   -.0109137
------------------------------------------------------------------------------

.         xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.         regife gdem ld lnregion xpers2,a(gwf_caseid year)factor(gwf_caseid yea
> r,1)vce(cluster gwf_caseid)
The option factors() was renamed to ife(). In the future, please use the syntax 
> ife(gwf_caseid year,1) to specify the factor model.
The algorithm did not converge : convergence error is 7.4e-08 (tolerance 1.0e-09
> )
Allow for more iterations with the option maxiter

REGIFE                                            Number of obs   =       4535
Panel structure: gwf_caseid, year                 F(   3,    255) =       6.61
Factor dimension: 1                               Prob > F        =     0.0003
Converged: false                                  Root MSE        =     0.0736
                                                  Iterations      =      10000
------------------------------------------------------------------------------
        gdem | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
          ld |   .0116457   .0030083     3.87   0.000     .0057215      .01757
    lnregion |   .0034435   .0018634     1.85   0.066     -.000226     .007113
      xpers2 |  -.0093572   .0036771    -2.54   0.012    -.0165987   -.0021158
       _cons |  -.0094099   .0075022    -1.25   0.211     -.024184    .0053643
------------------------------------------------------------------------------

.         est store timedem6

.                   * Nonlinear models *
.         qui xtprobit gdem ld lnregion xpers2 m_ld m_lnregion m_xpers2 y_ld y_l
> nregion y_xpers2,vce(cluster gwf_caseid)

.         est store timedem7

.          local var = "d1960 d1970 d1980 d1990 d2000 time time2"

.          foreach v of local var {
  2.                 egen m_`v' =mean(`v') if sample==1,by(gwf_caseid)
  3.          }

.         qui xtprobit gdem ld lnregion xpers2 m_ld m_lnregion m_xpers2 d19* d20
>  m_d19* m_d20,vce(cluster gwf_caseid)

.         est store timedem8

.         qui xtprobit gdem ld lnregion xpers2 m_ld m_lnregion m_xpers2 period*,
> vce(cluster gwf_caseid)

.         est store timedem9

.         qui xtprobit gdem ld lnregion xpers2 m_ld m_lnregion m_xpers2 time m_t
> ime,vce(cluster gwf_caseid)

.         est store timedem10

.         qui xtprobit gdem ld lnregion xpers2 m_ld m_lnregion m_xpers2 time tim
> e2 m_time m_time2,vce(cluster gwf_caseid)

.         est store timedem11

.         qui xtprobit gdem ld lnregion xpers2 m_ld m_lnregion m_xpers2 ///
>                 y_ld y_lnregion y_xpers2 mXy_ld mXy_lnregion mXy_xpers2,vce(cl
> uster gwf_caseid) 

.         est store timedem12

.         
.         label var xpers2 " "

.         coefplot (timedem1, msym(d))(timedem2, msym(t))(timedem3, msym(oh))(ti
> medem4, msym(plus))(timedem5, msym(P)) ///
>                         (timedem6, msym(d)),title("Fixed effects linear probab
> ility model", size(med))  relocate(xpers2 = 1)  ///
>                         drop(lnregion coldwar _cons ld m_* y_* mXy_* d19* d20*
>  period* time*) vertical xtit(Model specification,size(small)height(5)) ///
>                         order(xpers2) yline(0) grid(glcolor(gs13)) mfcolor(whi
> te) ylabel(-.04(.02)0,labsize(vsmall)) ///
>                          levels(95 90)   xlab(.64 `""Year" "effects""' .78 `""
> Decade" "effects""' .925 `""Period" "effects""' 1.07 `""Linear" "time trend""'
>  ///
>                         1.21 `""Non-linear" "time trend""' 1.35 `""Interactive
> " "fixed effects""',labsize(small) )  ///
>                         ysize(3) xsize(3.5) xscale(range(0.62 1.35))saving(g1,
>  replace) ytit("Marginal effect estimate", size(small) ///
>                         height(4))legend(off)note("90 (thick) and 95 (thin) pe
> rcent confidence intervals",size(small)ring(1) pos(6))
(note:  named style P not found in class symbol, default attributes used)
(note:  named style med not found in class gsize, default attributes used)
file g1.gph saved

.                 
.                          
.                 gen n=_n

.                 gen beta=.
(4,559 missing values generated)

.                 gen hi=.
(4,559 missing values generated)

.                 gen lo=.
(4,559 missing values generated)

.                 gen hi90=.
(4,559 missing values generated)

.                 gen lo90=.
(4,559 missing values generated)

.                 local i=1

.                 forval v =7/12 {
  2.                         di "`v'"
  3.                         est restore timedem`v'   
  4.                         margins,dydx(xpers2)predict(pu0)  
  5.                         matrix beta =r(b)  
  6.                         local b = beta[1,1]
  7.                         qui replace beta=`b' if n==`i'
  8.                         margins,dydx(xpers2)predict(pu0)post
  9.                         matrix var = r(V) 
 10.                         local se =var[1,1]
 11.                         qui replace hi = `b' + sqrt(`se')*1.96 if n==`i'
 12.                         qui replace lo = `b' - sqrt(`se')*1.96 if n==`i'
 13.                         qui replace hi90 = `b' + sqrt(`se')*1.65 if n==`i'
 14.                         qui replace lo90 = `b' - sqrt(`se')*1.65 if n==`i'
 15.                         local i = `i' +1
 16.                 }
7
(results timedem7 are active now)

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(gdem=1 | u_i=0), predict(pu0)
dy/dx wrt:  xpers2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0277515   .0057125    -4.86   0.000    -.0389478   -.0165551
------------------------------------------------------------------------------

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(gdem=1 | u_i=0), predict(pu0)
dy/dx wrt:  xpers2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0277515   .0057125    -4.86   0.000    -.0389478   -.0165551
------------------------------------------------------------------------------
8
(results timedem8 are active now)

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(gdem=1 | u_i=0), predict(pu0)
dy/dx wrt:  xpers2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0187387    .007336    -2.55   0.011     -.033117   -.0043603
------------------------------------------------------------------------------

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(gdem=1 | u_i=0), predict(pu0)
dy/dx wrt:  xpers2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0187387    .007336    -2.55   0.011     -.033117   -.0043603
------------------------------------------------------------------------------
9
(results timedem9 are active now)

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(gdem=1 | u_i=0), predict(pu0)
dy/dx wrt:  xpers2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0267212   .0061463    -4.35   0.000    -.0387678   -.0146746
------------------------------------------------------------------------------

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(gdem=1 | u_i=0), predict(pu0)
dy/dx wrt:  xpers2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0267212   .0061463    -4.35   0.000    -.0387678   -.0146746
------------------------------------------------------------------------------
10
(results timedem10 are active now)

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(gdem=1 | u_i=0), predict(pu0)
dy/dx wrt:  xpers2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0232616   .0055345    -4.20   0.000    -.0341091   -.0124142
------------------------------------------------------------------------------

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(gdem=1 | u_i=0), predict(pu0)
dy/dx wrt:  xpers2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0232616   .0055345    -4.20   0.000    -.0341091   -.0124142
------------------------------------------------------------------------------
11
(results timedem11 are active now)

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(gdem=1 | u_i=0), predict(pu0)
dy/dx wrt:  xpers2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0215574   .0053917    -4.00   0.000    -.0321249     -.01099
------------------------------------------------------------------------------

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(gdem=1 | u_i=0), predict(pu0)
dy/dx wrt:  xpers2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0215574   .0053917    -4.00   0.000    -.0321249     -.01099
------------------------------------------------------------------------------
12
(results timedem12 are active now)

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(gdem=1 | u_i=0), predict(pu0)
dy/dx wrt:  xpers2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0276089   .0050437    -5.47   0.000    -.0374944   -.0177235
------------------------------------------------------------------------------

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(gdem=1 | u_i=0), predict(pu0)
dy/dx wrt:  xpers2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0276089   .0050437    -5.47   0.000    -.0374944   -.0177235
------------------------------------------------------------------------------

.                 graph twoway (rspike hi lo n if n<=6,lcolor(blue*.5)lwidth(thi
> n) ///
>                         xtitle("Model specification",size(small)height(6)) ///
>                         yline(0,lp(dash)) ytitle("Marginal effect estimate", /
> //
>                         size(small) height(5))ylab(-.04(.02)0,)title("Correlat
> ed random effects probit",size(med))) ///
>                         (rspike hi90 lo90 n if n<=6,lcolor(blue*1) lwidth(medt
> hick)) ///
>                         (scatter beta n if  n<=6,msym(dot)lpattern(solid)mcolo
> r(blue*1.5) ///
>                         xscale(range(0.75 6.25))legend(lab(1 "95 ci")lab(3 "Es
> timate") size(small)pos(6)col(3)ring(1)) ///
>                         legend(order(3 1))saving(g2.gph,replace) xlab(1 `""Yea
> r" "effects""' 2 `""Decade" "effects""' ///
>                         3 `""Period" "effects""' 4 `""Linear" "time trend""' /
> //
>                         5 `""Non-linear" "time trend""' 6 `""Interactive" "fix
> ed effects""'))
(note:  named style dot not found in class symbol, default attributes used)
(note:  named style med not found in class gsize, default attributes used)
file g2.gph saved

.                 gr combine g1.gph g2.gph,xsize(6)ysize(3)
(note:  named style P not found in class symbol, default attributes used)
(note:  named style med not found in class gsize, default attributes used)
(note:  named style dot not found in class symbol, default attributes used)
(note:  named style med not found in class gsize, default attributes used)

.                                         graph export "$dir\time-dem.pdf", as(p
> df) replace  
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\time-dem.pdf saved as PDF format

. 
.                         
.         * Alternative Fixed effects *
.         qui reghdfe gdem ld lnregion xpers2,a(gwf_caseid year)cluster(gwf_case
> id)

.         est store fedem1

.         lincom xpers2

 ( 1)  xpers2 = 0

------------------------------------------------------------------------------
        gdem | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0231457    .006422    -3.60   0.000    -.0357926   -.0104989
------------------------------------------------------------------------------

.         qui reghdfe gdem ld lnregion xpers2,a(cowcode year)cluster(gwf_caseid)

.         est store fedem2

.         lincom xpers2

 ( 1)  xpers2 = 0

------------------------------------------------------------------------------
        gdem | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0194106    .004172    -4.65   0.000    -.0276234   -.0111978
------------------------------------------------------------------------------

.         qui reghdfe gdem lt lnregion xpers2,a(gwf_leaderid year)cluster(gwf_le
> aderid)

.         est store fedem3

.         lincom xpers2

 ( 1)  xpers2 = 0

------------------------------------------------------------------------------
        gdem | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0106737   .0055664    -1.92   0.056    -.0216157    .0002684
------------------------------------------------------------------------------

.         qui xi:xtreg gdem i.year ld lnregion xpers2,i(gwf_caseid)cluster(gwf_c
> aseid)

.         est store fedem4

.         lincom xpers2

 ( 1)  xpers2 = 0

------------------------------------------------------------------------------
        gdem | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0201973   .0055311    -3.65   0.000     -.031038   -.0093566
------------------------------------------------------------------------------

.         qui xi:xtreg gdem i.year ld lnregion xpers2,i(cowcode)cluster(cowcode)

.         est store fedem5

.         lincom xpers2

 ( 1)  xpers2 = 0

------------------------------------------------------------------------------
        gdem | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0109993   .0025252    -4.36   0.000    -.0159486   -.0060501
------------------------------------------------------------------------------

.         qui xi:xtreg gdem i.year lt lnregion xpers2,i(gwf_leaderid)cluster(gwf
> _leaderid)

.         est store fedem6

.         lincom xpers2

 ( 1)  xpers2 = 0

------------------------------------------------------------------------------
        gdem | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0155599   .0049459    -3.15   0.002    -.0252537    -.005866
------------------------------------------------------------------------------

.         
.         coefplot (fedem1, msym(d))(fedem2, msym(t))(fedem3, msym(oh))(fedem4, 
> msym(plus))(fedem5, msym(P)) ///
>                 (fedem6, msym(d)),title("Linear probability models", size(med)
> )   ///
>                 drop(lnregion ld lt _Iyear_* _cons)   xtit(Model specification
> ,size(small)height(5)) ///
>                 order(xpers2) xline(0) grid(glcolor(gs13)) mfcolor(white) xlab
> el(-.04(.02)0,labsize(vsmall)) ///
>                 levels(95 90)  ysize(3) xsize(3.5)   xtit("Marginal effect est
> imate", size(small) ///
>                 height(4))legend(lab(3 "Regime FE")lab(6 "Country FE")lab(9 "L
> eader FE") ////
>                 lab(12 "Regime RE")lab(15 "Country RE")lab(18 "Leader RE") ) /
> //
>                 note("90 (thick) and 95 (thin) percent confidence intervals; y
> ear effects in all specifications",size(vsmall)ring(1) pos(6))
(note:  named style P not found in class symbol, default attributes used)
(note:  named style med not found in class gsize, default attributes used)

.         graph export "$dir\dem-effects.pdf", as(pdf) replace  
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\dem-effects.pdf saved as PDF format

.         
.  
.  
.         * All regime collapse *
.         gen fail = gwf_case_fail

.         recode fail (1=0) if gdem==1
(103 changes made to fail)

.         qui reghdfe fail ld lnregion xpers2,a(gwf_caseid  year)cluster(gwf_cas
> eid)

.         lincom xpers2

 ( 1)  xpers2 = 0

------------------------------------------------------------------------------
        fail | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0084689   .0060464     1.40   0.163    -.0034383     .020376
------------------------------------------------------------------------------

.         est store fail1

.         xtsum fail if e(sample)==1

Variable         |      Mean   Std. dev.       Min        Max |    Observations
-----------------+--------------------------------------------+----------------
fail     overall |  .0246968   .1552166          0          1 |     N =    4535
         between |             .1677133          0          1 |     n =     481
         within  |             .1353716  -.4753032   .9976698 | T-bar = 9.42827

.         xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.         qui xtprobit fail ld lnregion period* xpers2 m_ld m_lnregion m_xpers2 
> m_period*, vce(cluster gwf_caseid)

.         margins,dydx(xpers2) 

Average marginal effects                                 Number of obs = 4,559
Model VCE: Robust

Expression: Pr(fail=1), predict(pr)
dy/dx wrt:  xpers2

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |   .0087519   .0044578     1.96   0.050     .0000147     .017489
------------------------------------------------------------------------------

.         lincom xpers2

 ( 1)  [fail]xpers2 = 0

------------------------------------------------------------------------------
        fail | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .2563079   .1408097     1.82   0.069     -.019674    .5322897
------------------------------------------------------------------------------

.         est store fail2

.         xtsum fail if e(sample)==1

Variable         |      Mean   Std. dev.       Min        Max |    Observations
-----------------+--------------------------------------------+----------------
fail     overall |  .0263216   .1601073          0          1 |     N =    4559
         between |             .1987484          0          1 |     n =     280
         within  |             .1423567  -.4736784   1.004099 | T-bar = 16.2821

.         estout fail1 fail2 using TableD1.tex, cells(b(star  fmt(%9.4f)) se(par
>  fmt(%9.3f))) ///
>                 stats(r2 N N_clust) style(tex) replace label starlevels(* 0.05
> ) title(\label{tabD1})
(file TableD1.tex not found)
(output written to TableD1.tex)

.   
.         *** Cox duration model ***
.         use temp-id,clear

.         stset gwf_case_duration, failure(gdem) id(gwf_caseid)

Survival-time data settings

           ID variable: gwf_caseid
         Failure event: gdem!=0 & gdem<.
Observed time interval: (gwf_case_duration[_n-1], gwf_case_duration]
     Exit on or before: failure

--------------------------------------------------------------------------
      4,559  total observations
          0  exclusions
--------------------------------------------------------------------------
      4,559  observations remaining, representing
        280  subjects
        103  failures in single-failure-per-subject data
      5,122  total analysis time at risk and under observation
                                                At risk from t =         0
                                     Earliest observed entry t =         0
                                          Last observed exit t =       105

.         stcoxkm if gwf_case_duration<=100, by(treat) obs1opts(lcol(red)lpat(so
> lid)mcol(red)msym(P)c(J)) ///
>                 obs2opts(lcol(blue)lpat(solid)mcol(blue)msym(P)c(J))  ///
>                 pred1opts(lcol(red)lpat(dash)mcol(red)msym(Oh)c(J)) ///
>                 pred2opts(lcol(blue)lpat(dash)mcol(blue)msym(Oh)c(J)) ///
>                 legend(pos(1)ring(0))ties(efron)xtit("Regime duration, years")
>  ///
>                 note(Binary treatment is Security personalism greater than the
>  sample median value,size(vsmall)pos(6))

        Failure _d: gdem
  Analysis time _t: gwf_case_duration
       ID variable: gwf_caseid
(note:  named style P not found in class symbol, default attributes used)
(note:  named style P not found in class symbol, default attributes used)

.                 graph export "$dir\dem-coxkm.pdf", as(pdf)   replace
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\dem-coxkm.pdf saved as PDF format

.         stcox period* lnregion xpers2,vce(cluster gwf_caseid)nohr

        Failure _d: gdem
  Analysis time _t: gwf_case_duration
       ID variable: gwf_caseid

Iteration 0:   log pseudolikelihood = -493.46514
Iteration 1:   log pseudolikelihood = -462.43947
Iteration 2:   log pseudolikelihood = -456.61702
Iteration 3:   log pseudolikelihood = -456.53828
Iteration 4:   log pseudolikelihood = -456.53826
Refining estimates:
Iteration 0:   log pseudolikelihood = -456.53826

Cox regression with Breslow method for ties

No. of subjects =   280                                 Number of obs =  4,559
No. of failures =   103
Time at risk    = 5,122
                                                        Wald chi2(14) =  96.64
Log pseudolikelihood = -456.53826                       Prob > chi2   = 0.0000

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
          _t | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     period1 |    .286004   .7064108     0.40   0.686    -1.098536    1.670544
     period2 |   .8569174   .5873031     1.46   0.145    -.2941755     2.00801
     period3 |    .183598   .6042835     0.30   0.761    -1.000776    1.367972
     period4 |  -.4580581   .7079899    -0.65   0.518    -1.845693    .9295766
     period5 |   .0155934   .6186251     0.03   0.980     -1.19689    1.228076
     period6 |   .6646063   .5451845     1.22   0.223    -.4039358    1.733148
     period7 |   .5682245    .564841     1.01   0.314    -.5388435    1.675292
     period8 |   1.628235   .4810969     3.38   0.001     .6853024    2.571167
     period9 |   1.638173   .4965331     3.30   0.001     .6649856    2.611359
    period10 |   1.346955   .5091373     2.65   0.008     .3490643    2.344846
    period11 |   .7516741    .595125     1.26   0.207    -.4147495    1.918098
    period12 |   1.041562   .6507605     1.60   0.109    -.2339049    2.317029
    lnregion |   .2289678    .097253     2.35   0.019     .0383554    .4195803
      xpers2 |  -.4714681    .105963    -4.45   0.000    -.6791517   -.2637845
------------------------------------------------------------------------------

.         est store demcox1

.                 * Cox, within transform *
.         forval i =1/12{
  2.                 egen m_period`i'=mean(period`i'),by(gwf_caseid)
  3.         }

.         stcox lnregion period* xpers2 m_lnregion m_period* m_xpers2,vce(cluste
> r gwf_caseid)     nohr

        Failure _d: gdem
  Analysis time _t: gwf_case_duration
       ID variable: gwf_caseid

Iteration 0:   log pseudolikelihood = -493.46514
Iteration 1:   log pseudolikelihood = -409.29357
Iteration 2:   log pseudolikelihood = -361.90768
Iteration 3:   log pseudolikelihood = -345.95018
Iteration 4:   log pseudolikelihood = -343.77416
Iteration 5:   log pseudolikelihood = -343.71705
Iteration 6:   log pseudolikelihood =   -343.717
Refining estimates:
Iteration 0:   log pseudolikelihood =   -343.717

Cox regression with Breslow method for ties

No. of subjects =   280                                 Number of obs =  4,559
No. of failures =   103
Time at risk    = 5,122
                                                        Wald chi2(28) = 189.34
Log pseudolikelihood = -343.717                         Prob > chi2   = 0.0000

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
          _t | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
    lnregion |    .065516   .1224985     0.53   0.593    -.1745766    .3056086
     period1 |    3.26178   1.058889     3.08   0.002     1.186395    5.337164
     period2 |   7.929702   1.592786     4.98   0.000       4.8079     11.0515
     period3 |    10.4489    1.73324     6.03   0.000     7.051812    13.84599
     period4 |   12.62457    1.84752     6.83   0.000     9.003496    16.24564
     period5 |   16.93679   2.086055     8.12   0.000      12.8482    21.02539
     period6 |   20.61542   2.347426     8.78   0.000     16.01455     25.2163
     period7 |   23.19595    2.66234     8.71   0.000     17.97786    28.41404
     period8 |   26.82605   2.953316     9.08   0.000     21.03766    32.61444
     period9 |   30.09361   3.313669     9.08   0.000     23.59893    36.58828
    period10 |   32.91831   3.835985     8.58   0.000     25.39992    40.43671
    period11 |   34.84864    4.12691     8.44   0.000     26.76004    42.93723
    period12 |   36.68365   4.353294     8.43   0.000     28.15135    45.21595
      xpers2 |  -.9511313   .2025864    -4.69   0.000    -1.348193   -.5540692
  m_lnregion |  -.1781365   .3168109    -0.56   0.574    -.7990745    .4428016
   m_period1 |  -4.250505   1.850369    -2.30   0.022    -7.877162   -.6238487
   m_period2 |  -8.907257   1.939395    -4.59   0.000     -12.7084   -5.106113
   m_period3 |  -11.16436   1.907433    -5.85   0.000    -14.90286   -7.425864
   m_period4 |  -13.99446   2.103499    -6.65   0.000    -18.11724   -9.871678
   m_period5 |  -18.42087   2.215639    -8.31   0.000    -22.76345    -14.0783
   m_period6 |  -21.56259   2.449612    -8.80   0.000    -26.36374   -16.76144
   m_period7 |  -23.43308    2.71886    -8.62   0.000    -28.76195   -18.10421
   m_period8 |  -26.53726   3.023983    -8.78   0.000    -32.46416   -20.61036
   m_period9 |  -29.84778   3.348856    -8.91   0.000    -36.41141   -23.28414
  m_period10 |  -32.76971   3.915717    -8.37   0.000    -40.44437   -25.09504
  m_period11 |  -34.87606   3.995841    -8.73   0.000    -42.70776   -27.04435
  m_period12 |  -36.90555   4.359726    -8.47   0.000    -45.45045   -28.36064
    m_xpers2 |   .6741608   .2125433     3.17   0.002     .2575836    1.090738
------------------------------------------------------------------------------

.         est store demcox2

.                 * Cox, with shared frailty by regime *
.         gen ldXlnregion=ld*lnregion

.         stcox period* lnregion xpers2 ldXlnregion,   shared(gwf_caseid)nohr

        Failure _d: gdem
  Analysis time _t: gwf_case_duration
       ID variable: gwf_caseid

Fitting comparison Cox model ...

Estimating frailty variance:
Iteration 0:   log profile likelihood = -448.96006  
Iteration 1:   log profile likelihood = -448.96006  (not concave)
Iteration 2:   log profile likelihood = -448.95986  
Iteration 3:   log profile likelihood = -448.95982  (backed up)
Iteration 4:   log profile likelihood = -448.95982  
Iteration 5:   log profile likelihood = -448.95982  (backed up)
numerical derivatives are approximate
flat or discontinuous region encountered
Iteration 6:   log profile likelihood =  -448.9598  
numerical derivatives are approximate
flat or discontinuous region encountered
Iteration 7:   log profile likelihood = -448.95692  
numerical derivatives are approximate
flat or discontinuous region encountered
Iteration 8:   log profile likelihood = -448.95692  

Fitting final Cox model:
Iteration 0:   log likelihood = -493.46199
Iteration 1:   log likelihood = -455.55812
Iteration 2:   log likelihood = -449.03828
Iteration 3:   log likelihood = -448.95692
Iteration 4:   log likelihood = -448.95692
Refining estimates:
Iteration 0:   log likelihood = -448.95692

Cox regression with Breslow method for ties
Gamma shared frailty                                Number of obs     =  4,559
Group variable: gwf_caseid                          Number of groups  =    280
                                                    Obs per group:   
No. of subjects =   280                                           min =      1
No. of failures =   103                                           avg =     16
Time at risk    = 5,122                                           max =     65
                                                    Wald chi2(15)     =  81.96
Log likelihood = -448.95692                         Prob > chi2       = 0.0000

------------------------------------------------------------------------------
          _t | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     period1 |   .1474919   .7111874     0.21   0.836     -1.24641    1.541394
     period2 |   .7698928   .5803538     1.33   0.185    -.3675797    1.907365
     period3 |  -.0058571   .6093588    -0.01   0.992    -1.200178    1.188464
     period4 |  -.5059497   .7084917    -0.71   0.475    -1.894568    .8826686
     period5 |   .0133115   .6079937     0.02   0.983    -1.178334    1.204957
     period6 |   .6289007   .5587148     1.13   0.260    -.4661603    1.723962
     period7 |   .5698159   .5797982     0.98   0.326    -.5665676    1.706199
     period8 |    1.65077   .4955742     3.33   0.001     .6794624    2.622077
     period9 |   1.727576   .4894362     3.53   0.000      .768299    2.686854
    period10 |   1.375999   .5153373     2.67   0.008     .3659565    2.386042
    period11 |   .8586352   .5955766     1.44   0.149    -.3086735    2.025944
    period12 |    .987228   .6127693     1.61   0.107    -.2137778    2.188234
    lnregion |  -.5218271   .2398953    -2.18   0.030    -.9920133   -.0516409
      xpers2 |  -.4809995   .1092822    -4.40   0.000    -.6951888   -.2668103
 ldXlnregion |   .3195584   .0892594     3.58   0.000     .1446133    .4945035
-------------+----------------------------------------------------------------
       theta |   7.34e-10   8.74e-21
------------------------------------------------------------------------------
LR test of theta=0: chibar2(01) = 0.01                 Prob >= chibar2 = 0.468

Note: Standard errors of regression parameters are conditional on theta.

.         est store demcox3

.         estat phtest, rank detail

Test of proportional-hazards assumption

Time function: Rank of analysis time
--------------------------------------------------------
             |        rho     chi2       df    Prob>chi2
-------------+------------------------------------------
     period1 |   -0.02768     0.08        1       0.7789
     period2 |    0.08501     0.74        1       0.3905
     period3 |   -0.03529     0.13        1       0.7209
     period4 |   -0.04954     0.25        1       0.6170
     period5 |    0.15354     2.35        1       0.1254
     period6 |    0.13110     1.72        1       0.1893
     period7 |    0.04096     0.17        1       0.6768
     period8 |    0.06941     0.48        1       0.4864
     period9 |    0.14486     2.17        1       0.1411
    period10 |    0.08164     0.67        1       0.4130
    period11 |   -0.06828     0.47        1       0.4907
    period12 |   -0.24855     7.63        1       0.0057
    lnregion |   -0.07560     0.60        1       0.4386
      xpers2 |   -0.02915     0.09        1       0.7700
 ldXlnregion |    0.08531     0.79        1       0.3745
-------------+------------------------------------------
 Global test |               35.80       15       0.0019
--------------------------------------------------------

.                 * Cox, with strata by country *
.         stcox period* lnregion xpers2, vce(cluster gwf_caseid)  strata(cowcode
> ) nohr

        Failure _d: gdem
  Analysis time _t: gwf_case_duration
       ID variable: gwf_caseid

Iteration 0:   log pseudolikelihood = -64.040406
Iteration 1:   log pseudolikelihood = -43.146439
Iteration 2:   log pseudolikelihood = -41.377374
Iteration 3:   log pseudolikelihood = -41.236227
Iteration 4:   log pseudolikelihood = -41.234616
Iteration 5:   log pseudolikelihood = -41.234616
Refining estimates:
Iteration 0:   log pseudolikelihood = -41.234616

Stratified Cox regression with Breslow method for ties
Strata variable: cowcode

No. of subjects =   280                                 Number of obs =  4,559
No. of failures =   103
Time at risk    = 5,122
                                                        Wald chi2(14) =  78.78
Log pseudolikelihood = -41.234616                       Prob > chi2   = 0.0000

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
          _t | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     period1 |  -.2198114   .8312854    -0.26   0.791    -1.849101    1.409478
     period2 |  -.0290493   .7828919    -0.04   0.970    -1.563489    1.505391
     period3 |   .0253384   .8920469     0.03   0.977    -1.723041    1.773718
     period4 |  -1.145598   .7646459    -1.50   0.134    -2.644276    .3530802
     period5 |  -1.969244   1.136176    -1.73   0.083    -4.196107    .2576192
     period6 |   .1279027   .7793982     0.16   0.870     -1.39969    1.655495
     period7 |   .1991076    .632968     0.31   0.753    -1.041487    1.439702
     period8 |   1.945906   .7509067     2.59   0.010     .4741559    3.417656
     period9 |   2.949441   .7258709     4.06   0.000      1.52676    4.372122
    period10 |     3.6359   1.173175     3.10   0.002     1.336519    5.935282
    period11 |   2.112218   .9292396     2.27   0.023      .290942    3.933494
    period12 |   4.420219   .8979886     4.92   0.000     2.660193    6.180244
    lnregion |  -.6779527   .1990313    -3.41   0.001    -1.068047   -.2878584
      xpers2 |  -.5175682   .2513516    -2.06   0.039    -1.010208   -.0249281
------------------------------------------------------------------------------

.         estat phtest, rank detail

Test of proportional-hazards assumption

Time function: Rank of analysis time
--------------------------------------------------------
             |        rho     chi2       df    Prob>chi2
-------------+------------------------------------------
     period1 |    0.04819     0.29        1       0.5871
     period2 |    0.07121     0.53        1       0.4649
     period3 |    0.02333     0.10        1       0.7485
     period4 |    0.02659     0.07        1       0.7932
     period5 |    0.04576     0.32        1       0.5725
     period6 |    0.04799     0.23        1       0.6341
     period7 |    0.04146     0.17        1       0.6797
     period8 |    0.03394     0.10        1       0.7573
     period9 |    0.05800     0.29        1       0.5890
    period10 |   -0.00615     0.01        1       0.9386
    period11 |    0.02613     0.09        1       0.7596
    period12 |    0.04015     0.11        1       0.7452
    lnregion |    0.07252     0.55        1       0.4595
      xpers2 |   -0.04331     0.30        1       0.5811
-------------+------------------------------------------
 Global test |                1.99       14       0.9999
--------------------------------------------------------
Note: Robust variance–covariance matrix used.

.         est store demcox4

.         stcox period* lnregion lt treat, vce(cluster gwf_caseid)  strata(cowco
> de)nohr

        Failure _d: gdem
  Analysis time _t: gwf_case_duration
       ID variable: gwf_caseid

Iteration 0:   log pseudolikelihood = -64.040406
Iteration 1:   log pseudolikelihood = -42.970948
Iteration 2:   log pseudolikelihood = -41.018521
Iteration 3:   log pseudolikelihood = -40.831086
Iteration 4:   log pseudolikelihood = -40.828112
Iteration 5:   log pseudolikelihood = -40.828111
Refining estimates:
Iteration 0:   log pseudolikelihood = -40.828111

Stratified Cox regression with Breslow method for ties
Strata variable: cowcode

No. of subjects =   280                                 Number of obs =  4,559
No. of failures =   103
Time at risk    = 5,122
                                                        Wald chi2(15) =  62.62
Log pseudolikelihood = -40.828111                       Prob > chi2   = 0.0000

                           (Std. err. adjusted for 280 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
          _t | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     period1 |  -.1944404   .7971113    -0.24   0.807     -1.75675    1.367869
     period2 |    .007247   .8272325     0.01   0.993    -1.614099    1.628593
     period3 |    .188485   .8510796     0.22   0.825      -1.4796     1.85657
     period4 |  -.8981398   .7284012    -1.23   0.218     -2.32578    .5295002
     period5 |  -2.012813      1.127    -1.79   0.074    -4.221692    .1960656
     period6 |   .2323735   .7851183     0.30   0.767     -1.30643    1.771177
     period7 |   .1712827   .7227277     0.24   0.813    -1.245238    1.587803
     period8 |   1.969296    .751562     2.62   0.009     .4962613     3.44233
     period9 |   3.277233   .7196234     4.55   0.000     1.866797    4.687669
    period10 |   4.344697   1.584239     2.74   0.006     1.239646    7.449748
    period11 |   2.188698   .9736446     2.25   0.025     .2803892    4.097006
    period12 |   4.674975   .8580956     5.45   0.000     2.993139    6.356812
    lnregion |  -.6787199   .2073595    -3.27   0.001    -1.085137   -.2723027
          lt |  -.4883203    .326579    -1.50   0.135    -1.128403    .1517628
       treat |  -.8618602   .5141757    -1.68   0.094    -1.869626    .1459056
------------------------------------------------------------------------------

.         estat phtest, rank detail

Test of proportional-hazards assumption

Time function: Rank of analysis time
--------------------------------------------------------
             |        rho     chi2       df    Prob>chi2
-------------+------------------------------------------
     period1 |    0.06598     0.56        1       0.4552
     period2 |    0.10359     1.62        1       0.2038
     period3 |    0.04420     0.41        1       0.5205
     period4 |    0.04434     0.21        1       0.6457
     period5 |    0.05919     0.54        1       0.4637
     period6 |    0.07181     0.67        1       0.4115
     period7 |    0.07958     1.00        1       0.3177
     period8 |    0.03323     0.08        1       0.7839
     period9 |    0.06267     0.46        1       0.4958
    period10 |   -0.03147     0.26        1       0.6094
    period11 |    0.05056     0.46        1       0.4972
    period12 |    0.07263     0.24        1       0.6262
    lnregion |    0.09026     0.92        1       0.3382
          lt |    0.08396     1.10        1       0.2938
       treat |    0.00020     0.00        1       0.9978
-------------+------------------------------------------
 Global test |                3.77       15       0.9984
--------------------------------------------------------
Note: Robust variance–covariance matrix used.

.         stcox period* lnregion xpers2 loggdp lpop logoil, vce(cluster gwf_case
> id)  strata(cowcode)nohr

        Failure _d: gdem
  Analysis time _t: gwf_case_duration
       ID variable: gwf_caseid

Iteration 0:   log pseudolikelihood = -58.292287
Iteration 1:   log pseudolikelihood =  -37.71007
Iteration 2:   log pseudolikelihood = -34.957819
Iteration 3:   log pseudolikelihood = -34.427753
Iteration 4:   log pseudolikelihood = -34.386802
Iteration 5:   log pseudolikelihood = -34.386493
Iteration 6:   log pseudolikelihood = -34.386493
Refining estimates:
Iteration 0:   log pseudolikelihood = -34.386493

Stratified Cox regression with Breslow method for ties
Strata variable: cowcode

No. of subjects =   277                                 Number of obs =  4,485
No. of failures =   101
Time at risk    = 4,688
                                                        Wald chi2(17) =  86.08
Log pseudolikelihood = -34.386493                       Prob > chi2   = 0.0000

                           (Std. err. adjusted for 277 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
          _t | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     period1 |   1.163588   1.084544     1.07   0.283    -.9620789    3.289255
     period2 |   2.263125   1.691857     1.34   0.181    -1.052854    5.579103
     period3 |   2.770639   2.023094     1.37   0.171    -1.194551     6.73583
     period4 |   2.094543   2.024552     1.03   0.301    -1.873505    6.062592
     period5 |   3.023426   2.708759     1.12   0.264    -2.285645    8.332497
     period6 |   4.724771   2.898733     1.63   0.103    -.9566407    10.40618
     period7 |   5.207718   2.990082     1.74   0.082    -.6527355    11.06817
     period8 |   8.518867   3.530313     2.41   0.016     1.599581    15.43815
     period9 |   9.927294   4.424295     2.24   0.025     1.255836    18.59875
    period10 |   10.69772   4.284865     2.50   0.013     2.299536     19.0959
    period11 |   9.953249   4.584686     2.17   0.030     .9674283    18.93907
    period12 |   13.75673   5.437533     2.53   0.011      3.09936     24.4141
    lnregion |   -.768706    .221013    -3.48   0.001    -1.201884   -.3355285
      xpers2 |  -.8099151   .3323394    -2.44   0.015    -1.461288   -.1585418
      loggdp |  -1.420548   .7704196    -1.84   0.065    -2.930543    .0894466
       lpopl |  -4.792071   3.047102    -1.57   0.116    -10.76428    1.180138
      logoil |  -4.906944   2.754582    -1.78   0.075    -10.30582    .4919373
------------------------------------------------------------------------------

.         est store demcox5

.         estat phtest, rank detail

Test of proportional-hazards assumption

Time function: Rank of analysis time
--------------------------------------------------------
             |        rho     chi2       df    Prob>chi2
-------------+------------------------------------------
     period1 |    0.09215     1.53        1       0.2168
     period2 |    0.09565     2.13        1       0.1448
     period3 |    0.07532     1.49        1       0.2224
     period4 |    0.09495     1.79        1       0.1815
     period5 |    0.09314     2.09        1       0.1485
     period6 |    0.09032     2.02        1       0.1557
     period7 |    0.09259     1.99        1       0.1582
     period8 |    0.09778     2.17        1       0.1406
     period9 |    0.08960     2.29        1       0.1298
    period10 |    0.08802     2.07        1       0.1499
    period11 |    0.08869     2.10        1       0.1470
    period12 |    0.09919     2.22        1       0.1363
    lnregion |    0.07714     0.59        1       0.4435
      xpers2 |   -0.07445     1.65        1       0.1989
      loggdp |   -0.07871     0.51        1       0.4761
       lpopl |   -0.09429     2.22        1       0.1359
      logoil |   -0.02989     0.14        1       0.7101
-------------+------------------------------------------
 Global test |                3.40       17       0.9998
--------------------------------------------------------
Note: Robust variance–covariance matrix used.

.         stcox period* lnregion xpers2 loggdp lpop logoil l1v2x_polyarchy l2v2x
> _polyarchy, vce(cluster gwf_caseid)   strata(cowcode)nohr 

        Failure _d: gdem
  Analysis time _t: gwf_case_duration
       ID variable: gwf_caseid

Iteration 0:   log pseudolikelihood = -58.292287
Iteration 1:   log pseudolikelihood = -36.959959
Iteration 2:   log pseudolikelihood = -33.787196
Iteration 3:   log pseudolikelihood =  -33.00292
Iteration 4:   log pseudolikelihood = -32.929585
Iteration 5:   log pseudolikelihood = -32.928796
Iteration 6:   log pseudolikelihood = -32.928796
Refining estimates:
Iteration 0:   log pseudolikelihood = -32.928796

Stratified Cox regression with Breslow method for ties
Strata variable: cowcode

No. of subjects =   277                                 Number of obs =  4,473
No. of failures =   101
Time at risk    = 4,676
                                                        Wald chi2(19) =  76.63
Log pseudolikelihood = -32.928796                       Prob > chi2   = 0.0000

                            (Std. err. adjusted for 277 clusters in gwf_caseid)
-------------------------------------------------------------------------------
              |               Robust
           _t | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
      period1 |   1.061851   1.265143     0.84   0.401    -1.417783    3.541485
      period2 |   2.748308   1.713711     1.60   0.109     -.610504     6.10712
      period3 |   2.837146   2.293719     1.24   0.216    -1.658461    7.332752
      period4 |   2.441884   2.012103     1.21   0.225    -1.501765    6.385534
      period5 |   3.935529   2.635619     1.49   0.135     -1.23019    9.101247
      period6 |   5.311959     2.7452     1.93   0.053    -.0685337    10.69245
      period7 |   6.295193   2.906198     2.17   0.030     .5991498    11.99124
      period8 |   9.513008   3.505507     2.71   0.007     2.642341    16.38368
      period9 |    11.6692   4.282468     2.72   0.006     3.275717    20.06269
     period10 |   12.37298   4.194678     2.95   0.003     4.151561     20.5944
     period11 |   12.12778   4.598485     2.64   0.008     3.114917    21.14065
     period12 |   16.28619   5.395108     3.02   0.003     5.711973    26.86041
     lnregion |  -.8874133   .2972399    -2.99   0.003    -1.469993   -.3048338
       xpers2 |  -.8523387   .4143045    -2.06   0.040    -1.664361   -.0403169
       loggdp |  -1.194471   .7703113    -1.55   0.121    -2.704253    .3153116
        lpopl |    -5.6663   3.031029    -1.87   0.062    -11.60701    .2744077
       logoil |  -4.869248   2.375584    -2.05   0.040    -9.525306   -.2131896
l1v2x_polya~y |   6.598226   6.458884     1.02   0.307    -6.060955    19.25741
l2v2x_polya~y |  -7.993153   5.484986    -1.46   0.145    -18.74353    2.757221
-------------------------------------------------------------------------------

.         est store demcox6

.         estout demcox* using TableD2.tex, cells(b(star  fmt(%9.4f)) se(par fmt
> (%9.3f))) ///
>                 stats(r2 N N_clust) style(tex) replace label starlevels(* 0.05
> ) title(\label{tabD2})
(file TableD2.tex not found)
(output written to TableD2.tex)

.         
.  
.                 **** Adding covariates, one at a time ****
.                 use temp-fe,clear

.                 recode debruin_affcount (5 4 3=3)
(252 changes made to debruin_affcount)

.                 spearman xpers2 v2x_clpol v2clkill v2cltort v2juhcind v2x_juco
> n v2x_frassoc_thick v2x_freexp_altinf v2x_partipdem v2x_libdem v2x_polyarchy
(obs=4554)

             |   xpers2 v2x_cl~l v2clkill v2cltort v2juhc~d v2x_ju~n v2x_fr~k
-------------+---------------------------------------------------------------
      xpers2 |   1.0000 
   v2x_clpol |  -0.3236   1.0000 
    v2clkill |  -0.3969   0.5047   1.0000 
    v2cltort |  -0.3467   0.5513   0.7997   1.0000 
   v2juhcind |  -0.2470   0.4358   0.2038   0.2687   1.0000 
   v2x_jucon |  -0.3535   0.4839   0.3541   0.4297   0.8111   1.0000 
v2x_frasso~k |  -0.2836   0.9217   0.3856   0.4358   0.4053   0.4333   1.0000 
v2x_freexp~f |  -0.2690   0.9501   0.4272   0.4882   0.4123   0.4496   0.8471 
v2x_partip~m |  -0.2507   0.7686   0.3765   0.3985   0.3936   0.4311   0.7646 
  v2x_libdem |  -0.3497   0.8164   0.5578   0.6016   0.6044   0.7278   0.7574 
v2x_polyar~y |  -0.2500   0.7712   0.3872   0.4363   0.3142   0.3712   0.7900 

             | v2x_fr~f v2x_pa~m v2x_li~m v2x_po~y
-------------+------------------------------------
v2x_freexp~f |   1.0000 
v2x_partip~m |   0.7472   1.0000 
  v2x_libdem |   0.7613   0.7417   1.0000 
v2x_polyar~y |   0.7218   0.8180   0.7856   1.0000 

.                 gen n=_n

.                 gen beta=.
(4,559 missing values generated)

.                 gen hi=.
(4,559 missing values generated)

.                 gen lo=.
(4,559 missing values generated)

.                 gen hi90=.
(4,559 missing values generated)

.                 gen lo90=.
(4,559 missing values generated)

.                 gen varname=""
(4,559 missing values generated)

.                 xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                 qui reghdfe gdem ld lnregion xpers2,a(gwf_caseid  year)cluster
> (gwf_caseid)

.                 nlcom _b[xpers2],post

       _nl_1: _b[xpers2]

------------------------------------------------------------------------------
        gdem | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       _nl_1 |  -.0231457    .006422    -3.60   0.000    -.0357325   -.0105589
------------------------------------------------------------------------------

.                 matrix beta =e(b)  

.                 global b = beta[1,1]    

.                 global num =40

.                 global cvar ="ld lnregion"

.                 global varA= "lpop loggdp logoil support gwf_mil leadermil sei
> zure_coup coup12 election ythbul4 wdipopurbmi wditrade"

.                 global varB= "polparcomp polpolcomp polexconst l1v2x_polyarchy
>  l2v2x_polyarchy l1v2x_partipdem l1v2x_freexp_altinf l1v2x_frassoc_thick "

.                 global varC= "l1v2x_clpol l1v2x_jucon l1v2juhcind l1e_v2x_neop
> at priordem legcomp inst excluded monoethnic multiethnic civwar grow"

.                 global varD= "milethnic_homo nmc_logmilper nmc_logmilex effect
> ivenumber debruin_cbcount debruin_ha_cbcount debruin_affcount lag_xongoing"

.                 local var = "$varA $varB $varC $varD"

.                 local i =1

.                 foreach v of local var {
  2.                         di "`v'"
  3.                         qui xtset gwf_caseid
  4.                         qui reghdfe gdem `v' ld lnregion xpers2,a(gwf_casei
> d  year)cluster(gwf_caseid)
  5.                         qui nlcom _b[xpers2],post
  6.                         matrix beta =e(b)  
  7.                         local b = beta[1,1]
  8.                         qui replace beta=`b' if n==`i'
  9.                         matrix var = e(V) 
 10.                         local se =var[1,1]
 11.                         qui replace hi = `b' + sqrt(`se')*1.96 if n==`i'
 12.                         qui replace lo = `b' - sqrt(`se')*1.96 if n==`i'
 13.                         qui replace hi90 = `b' + sqrt(`se')*1.65 if n==`i'
 14.                         qui replace lo90 = `b' - sqrt(`se')*1.65 if n==`i'
 15.                         qui replace varname = "`v'" if n==`i'
 16.                         local i = `i' +1
 17.                 }
lpop
loggdp
logoil
support
gwf_mil
leadermil
seizure_coup
coup12
election
ythbul4
wdipopurbmi
wditrade
polparcomp
polpolcomp
polexconst
l1v2x_polyarchy
l2v2x_polyarchy
l1v2x_partipdem
l1v2x_freexp_altinf
l1v2x_frassoc_thick
l1v2x_clpol
l1v2x_jucon
l1v2juhcind
l1e_v2x_neopat
priordem
legcomp
inst
excluded
monoethnic
multiethnic
civwar
grow
milethnic_homo
nmc_logmilper
nmc_logmilex
effectivenumber
debruin_cbcount
debruin_ha_cbcount
debruin_affcount
lag_xongoing

.                 label define varlab 1 "Population" 2 "GDP per capita (log)" 3 
> "Oil per capita (log)"  ///
>                         4 "Support party" 5 "Military junta" 6 "Military leade
> r" 7 "Coup seizure" 8 "Recent coup" 9 "Election" ///
>                         10 "Youth bulge" 11 "Urban pop." 12 "Trade"   13 "Parc
> omp" 14 "Polcomp" 15 "Exec. constr." ///
>                         16 "V-Polyarchy,t-1" 17 "V-Polyarchy,t-2" 18 "V-Partic
> ipation,t-1" 19 "V-Free express. info,t-1" ///
>                         20 "V-Free express. org,t-1" 21 "V-Civil lib.,t-1" 22 
> "V-Judical constr.,t-1" 23 "V-Judicial indep.,t-1" ///
>                         24 "V-Neopatrimonialism,t-1" 25 "Prior democracy" ///
>                         26 "Leg. comp." 27 "Institutions" 28 "Excluded pop" 29
>  "Monoethnic party" 30 "Multiethnic party" ///
>                         31 "Civil war" 32 "Growth" 33 "Ethnic homo military"  
> 34 "Military personnel (log)" ///
>                         35 "Military exp. (log)" 36 "Effective number" 37 "Cou
> nter-weights" 38 "Heavily armed counter-weights" ///
>                         39 "Affiliated paramilitaries" 40 "Ongoing protests,t-
> 1",replace

.                 label values n varlab

.                 twoway (scatter beta n if n<=$num,mcol(blue)yline($b,lcol(red)
> lpat(dash_dot))) ///
>                         (rspike hi lo n if n<=$num,lw(vthin)lcol(blue)) ///
>                         (rspike hi90 lo90 n if n<=$num,lcol(blue)lw(medium)yti
> tle("{&beta}{sub:Security personalization}", ///
>                         size(large)height(4)) ylab(-.06(.02)0) ///
>                         xtitle(Added covariate)yline(0,lpat(dash)lcol(gs6))xla
> b(1(1)$num,valuelabel angle(90))legend(off))

.                 graph export "$dir\dem-baseline-covariates.pdf", as(pdf)   rep
> lace
file
    C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-reproduct
    > ion\dem-baseline-covariates.pdf saved as PDF format

.                 
.                 *** IV-2SLS ***
.                 use temp-fe,clear

.                 xtset gwf_caseid year

Panel variable: gwf_caseid (unbalanced)
 Time variable: year, 1946 to 2010
         Delta: 1 unit

.                 forval i =1/4 {
  2.                         gen l`i'vdem = l`i'.v2x_polyarchy
  3.                 }
(282 missing values generated)
(538 missing values generated)
(772 missing values generated)
(990 missing values generated)

.                 gen militrank2=militrank^2

.                 xi:ivreghdfe gdem ld lnregion (xpers2=militrank militrank2),ab
> sorb(gwf_caseid year)cluster(gwf_caseid) 
(dropped 24 singleton observations)
(MWFE estimator converged in 10 iterations)

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on gwf_caseid

Number of clusters (gwf_caseid) =    256              Number of obs =     4535
                                                      F(  3,   255) =     6.69
                                                      Prob > F      =   0.0002
Total (centered) SS     =  72.68825982                Centered R2   =  -0.0498
Total (uncentered) SS   =  72.68825982                Uncentered R2 =  -0.0498
Residual SS             =  76.30818779                Root MSE      =    .1307

------------------------------------------------------------------------------
             |               Robust
        gdem | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0921356   .0794266    -1.16   0.247    -.2485512      .06428
          ld |   .0542987   .0263499     2.06   0.040     .0024076    .1061897
    lnregion |    .004196   .0033361     1.26   0.210    -.0023739    .0107658
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):              4.135
                                                   Chi-sq(2) P-val =    0.1265
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):               51.427
                         (Kleibergen-Paap rk Wald F statistic):          2.661
Stock-Yogo weak ID test critical values: 10% maximal IV size             19.93
                                         15% maximal IV size             11.59
                                         20% maximal IV size              8.75
                                         25% maximal IV size              7.25
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.009
                                                   Chi-sq(1) P-val =    0.9235
------------------------------------------------------------------------------
Instrumented:         xpers2
Included instruments: ld lnregion
Excluded instruments: militrank militrank2
Partialled-out:       _cons
                      nb: total SS, model F and R2s are after partialling-out;
                          any small-sample adjustments include partialled-out
                          variables in regressor count K
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  gwf_caseid |       256         256           0    *|
        year |        65           0          65     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.                 est store demiv1

.                 xi:ivreghdfe gdem ld lnregion l1vdem l2vdem (xpers2=militrank 
> militrank2),absorb(gwf_caseid year)cluster(gwf_caseid)   
(dropped 16 singleton observations)
(MWFE estimator converged in 10 iterations)

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics robust to heteroskedasticity and clustering on gwf_caseid

Number of clusters (gwf_caseid) =    218              Number of obs =     4004
                                                      F(  5,   217) =     7.70
                                                      Prob > F      =   0.0000
Total (centered) SS     =  61.23098209                Centered R2   =   0.0068
Total (uncentered) SS   =  61.23098209                Uncentered R2 =   0.0068
Residual SS             =  60.81385248                Root MSE      =    .1243

------------------------------------------------------------------------------
             |               Robust
        gdem | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0648903   .0687207    -0.94   0.346    -.2003359    .0705552
          ld |   .0488719   .0269856     1.81   0.072    -.0043157    .1020594
    lnregion |   .0051087   .0034478     1.48   0.140    -.0016868    .0119041
      l1vdem |    .381987   .1681888     2.27   0.024     .0504942    .7134797
      l2vdem |  -.0962944   .1510549    -0.64   0.524     -.394017    .2014281
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):              5.400
                                                   Chi-sq(2) P-val =    0.0672
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic):               71.172
                         (Kleibergen-Paap rk Wald F statistic):          3.599
Stock-Yogo weak ID test critical values: 10% maximal IV size             19.93
                                         15% maximal IV size             11.59
                                         20% maximal IV size              8.75
                                         25% maximal IV size              7.25
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.067
                                                   Chi-sq(1) P-val =    0.7951
------------------------------------------------------------------------------
Instrumented:         xpers2
Included instruments: ld lnregion l1vdem l2vdem
Excluded instruments: militrank militrank2
Partialled-out:       _cons
                      nb: total SS, model F and R2s are after partialling-out;
                          any small-sample adjustments include partialled-out
                          variables in regressor count K
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  gwf_caseid |       218         218           0    *|
        year |        63           0          63     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.                 est store demiv2

.                 
.                 xi:ivreg2h gdem i.year  ld lnregion (xpers2=militrank militran
> k2),fe cluster(gwf_caseid) partial(i.year) gmm2s
i.year            _Iyear_1946-2010    (naturally coded; _Iyear_1946 omitted)

Standard IV Results
Fixed Effects by(gwf_caseid), 280 groups

2-Step GMM estimation
---------------------

Estimates efficient for arbitrary heteroskedasticity and clustering on gwf_casei
> d
Statistics robust to heteroskedasticity and clustering on gwf_caseid

Number of clusters (gwf_caseid) = 280                 Number of obs =     4559
                                                      F(  3,   279) =     6.93
                                                      Prob > F      =   0.0002
Total (centered) SS     =  72.68826002                Centered R2   =  -0.0409
Total (uncentered) SS   =  72.68826002                Uncentered R2 =  -0.0409
Residual SS             =  75.66324411                Root MSE      =     .133

------------------------------------------------------------------------------
             |               Robust
        gdem | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0872308   .0598305    -1.46   0.145    -.2044965    .0300348
          ld |   .0527209   .0202766     2.60   0.009     .0129794    .0924623
    lnregion |   .0041591   .0032825     1.27   0.205    -.0022745    .0105927
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):              4.135
                                                   Chi-sq(2) P-val =    0.1265
------------------------------------------------------------------------------
Weak identification test (Kleibergen-Paap rk Wald F statistic):          2.662
Stock-Yogo weak ID test critical values: 10% maximal IV size             19.93
                                         15% maximal IV size             11.59
                                         20% maximal IV size              8.75
                                         25% maximal IV size              7.25
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.009
                                                   Chi-sq(1) P-val =    0.9235
------------------------------------------------------------------------------
Instrumented:         xpers2
Included instruments: ld lnregion
Excluded instruments: militrank militrank2
Partialled-out:       _Iyear_1947 _Iyear_1948 _Iyear_1949 _Iyear_1950
                      _Iyear_1951 _Iyear_1952 _Iyear_1953 _Iyear_1954
                      _Iyear_1955 _Iyear_1956 _Iyear_1957 _Iyear_1958
                      _Iyear_1959 _Iyear_1960 _Iyear_1961 _Iyear_1962
                      _Iyear_1963 _Iyear_1964 _Iyear_1965 _Iyear_1966
                      _Iyear_1967 _Iyear_1968 _Iyear_1969 _Iyear_1970
                      _Iyear_1971 _Iyear_1972 _Iyear_1973 _Iyear_1974
                      _Iyear_1975 _Iyear_1976 _Iyear_1977 _Iyear_1978
                      _Iyear_1979 _Iyear_1980 _Iyear_1981 _Iyear_1982
                      _Iyear_1983 _Iyear_1984 _Iyear_1985 _Iyear_1986
                      _Iyear_1987 _Iyear_1988 _Iyear_1989 _Iyear_1990
                      _Iyear_1991 _Iyear_1992 _Iyear_1993 _Iyear_1994
                      _Iyear_1995 _Iyear_1996 _Iyear_1997 _Iyear_1998
                      _Iyear_1999 _Iyear_2000 _Iyear_2001 _Iyear_2002
                      _Iyear_2003 _Iyear_2004 _Iyear_2005 _Iyear_2006
                      _Iyear_2007 _Iyear_2008 _Iyear_2009 _Iyear_2010
                      nb: small-sample adjustments account for
                          partialled-out variables
------------------------------------------------------------------------------

Warning: variables have been centered

IV with Generated Instruments only
Fixed Effects by(gwf_caseid), 280 groups
Instruments created from Z:
_Iyear_1947 _Iyear_1948 _Iyear_1949 _Iyear_1950 _Iyear_1951 _Iyear_1952 _Iyear_1
> 953 _Iyear_1954 _Iyear_1955 _Iyear_1956 _Iyear_1957 _Iyear_1958 _Iyear_1959 _I
> year_1960 _Iyear_1961 _Iyear_1962 _Iyear_1963 _Iyear_1964 _Iyear_1965 _Iyear_1
> 966 _Iyear_1967 _Iyear_1968 _Iyear_1969 _Iyear_1970 _Iyear_1971 _Iyear_1972 _I
> year_1973 _Iyear_1974 _Iyear_1975 _Iyear_1976 _Iyear_1977 _Iyear_1978 _Iyear_1
> 979 _Iyear_1980 _Iyear_1981 _Iyear_1982 _Iyear_1983 _Iyear_1984 _Iyear_1985 _I
> year_1986 _Iyear_1987 _Iyear_1988 _Iyear_1989 _Iyear_1990 _Iyear_1991 _Iyear_1
> 992 _Iyear_1993 _Iyear_1994 _Iyear_1995 _Iyear_1996 _Iyear_1997 _Iyear_1998 _I
> year_1999 _Iyear_2000 _Iyear_2001 _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2
> 005 _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009 _Iyear_2010 ld lnregion

2-Step GMM estimation
---------------------

Estimates efficient for arbitrary heteroskedasticity and clustering on gwf_casei
> d
Statistics robust to heteroskedasticity and clustering on gwf_caseid

Number of clusters (gwf_caseid) = 280                 Number of obs =     4559
                                                      F(  3,   279) =    11.06
                                                      Prob > F      =   0.0000
Total (centered) SS     =  72.68826002                Centered R2   =   0.0107
Total (uncentered) SS   =  72.68826002                Uncentered R2 =   0.0107
Residual SS             =  71.90891459                Root MSE      =    .1296

------------------------------------------------------------------------------
             |               Robust
        gdem | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0074816   .0019905    -3.76   0.000    -.0113828   -.0035804
          ld |   .0198753   .0036624     5.43   0.000     .0126972    .0270534
    lnregion |   .0007814   .0019495     0.40   0.689    -.0030395    .0046023
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):             84.954
                                                   Chi-sq(66) P-val =   0.0581
------------------------------------------------------------------------------
Weak identification test (Kleibergen-Paap rk Wald F statistic):         15.274
Stock-Yogo weak ID test critical values:  5% maximal IV relative bias    21.22
                                         10% maximal IV relative bias    11.01
                                         20% maximal IV relative bias     5.78
                                         30% maximal IV relative bias     4.00
                                         10% maximal IV size            172.11
                                         15% maximal IV size             88.07
                                         20% maximal IV size             59.74
                                         25% maximal IV size             45.57
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):        59.905
                                                   Chi-sq(65) P-val =   0.6555
------------------------------------------------------------------------------
Instrumented:         xpers2
Included instruments: ld lnregion
Excluded instruments: xpers2__Iyear_1947_g xpers2__Iyear_1948_g
                      xpers2__Iyear_1949_g xpers2__Iyear_1950_g
                      xpers2__Iyear_1951_g xpers2__Iyear_1952_g
                      xpers2__Iyear_1953_g xpers2__Iyear_1954_g
                      xpers2__Iyear_1955_g xpers2__Iyear_1956_g
                      xpers2__Iyear_1957_g xpers2__Iyear_1958_g
                      xpers2__Iyear_1959_g xpers2__Iyear_1960_g
                      xpers2__Iyear_1961_g xpers2__Iyear_1962_g
                      xpers2__Iyear_1963_g xpers2__Iyear_1964_g
                      xpers2__Iyear_1965_g xpers2__Iyear_1966_g
                      xpers2__Iyear_1967_g xpers2__Iyear_1968_g
                      xpers2__Iyear_1969_g xpers2__Iyear_1970_g
                      xpers2__Iyear_1971_g xpers2__Iyear_1972_g
                      xpers2__Iyear_1973_g xpers2__Iyear_1974_g
                      xpers2__Iyear_1975_g xpers2__Iyear_1976_g
                      xpers2__Iyear_1977_g xpers2__Iyear_1978_g
                      xpers2__Iyear_1979_g xpers2__Iyear_1980_g
                      xpers2__Iyear_1981_g xpers2__Iyear_1982_g
                      xpers2__Iyear_1983_g xpers2__Iyear_1984_g
                      xpers2__Iyear_1985_g xpers2__Iyear_1986_g
                      xpers2__Iyear_1987_g xpers2__Iyear_1988_g
                      xpers2__Iyear_1989_g xpers2__Iyear_1990_g
                      xpers2__Iyear_1991_g xpers2__Iyear_1992_g
                      xpers2__Iyear_1993_g xpers2__Iyear_1994_g
                      xpers2__Iyear_1995_g xpers2__Iyear_1996_g
                      xpers2__Iyear_1997_g xpers2__Iyear_1998_g
                      xpers2__Iyear_1999_g xpers2__Iyear_2000_g
                      xpers2__Iyear_2001_g xpers2__Iyear_2002_g
                      xpers2__Iyear_2003_g xpers2__Iyear_2004_g
                      xpers2__Iyear_2005_g xpers2__Iyear_2006_g
                      xpers2__Iyear_2007_g xpers2__Iyear_2008_g
                      xpers2__Iyear_2009_g xpers2__Iyear_2010_g xpers2_ld_g
                      xpers2_lnregion_g
Partialled-out:       _Iyear_1947 _Iyear_1948 _Iyear_1949 _Iyear_1950
                      _Iyear_1951 _Iyear_1952 _Iyear_1953 _Iyear_1954
                      _Iyear_1955 _Iyear_1956 _Iyear_1957 _Iyear_1958
                      _Iyear_1959 _Iyear_1960 _Iyear_1961 _Iyear_1962
                      _Iyear_1963 _Iyear_1964 _Iyear_1965 _Iyear_1966
                      _Iyear_1967 _Iyear_1968 _Iyear_1969 _Iyear_1970
                      _Iyear_1971 _Iyear_1972 _Iyear_1973 _Iyear_1974
                      _Iyear_1975 _Iyear_1976 _Iyear_1977 _Iyear_1978
                      _Iyear_1979 _Iyear_1980 _Iyear_1981 _Iyear_1982
                      _Iyear_1983 _Iyear_1984 _Iyear_1985 _Iyear_1986
                      _Iyear_1987 _Iyear_1988 _Iyear_1989 _Iyear_1990
                      _Iyear_1991 _Iyear_1992 _Iyear_1993 _Iyear_1994
                      _Iyear_1995 _Iyear_1996 _Iyear_1997 _Iyear_1998
                      _Iyear_1999 _Iyear_2000 _Iyear_2001 _Iyear_2002
                      _Iyear_2003 _Iyear_2004 _Iyear_2005 _Iyear_2006
                      _Iyear_2007 _Iyear_2008 _Iyear_2009 _Iyear_2010
                      nb: small-sample adjustments account for
                          partialled-out variables
------------------------------------------------------------------------------

Warning: variables have been centered

IV with Generated Instruments and External Instruments
Fixed Effects by(gwf_caseid), 280 groups
Testing Orthogonality of Instruments created from Z:
_Iyear_1947 _Iyear_1948 _Iyear_1949 _Iyear_1950 _Iyear_1951 _Iyear_1952 _Iyear_1
> 953 _Iyear_1954 _Iyear_1955 _Iyear_1956 _Iyear_1957 _Iyear_1958 _Iyear_1959 _I
> year_1960 _Iyear_1961 _Iyear_1962 _Iyear_1963 _Iyear_1964 _Iyear_1965 _Iyear_1
> 966 _Iyear_1967 _Iyear_1968 _Iyear_1969 _Iyear_1970 _Iyear_1971 _Iyear_1972 _I
> year_1973 _Iyear_1974 _Iyear_1975 _Iyear_1976 _Iyear_1977 _Iyear_1978 _Iyear_1
> 979 _Iyear_1980 _Iyear_1981 _Iyear_1982 _Iyear_1983 _Iyear_1984 _Iyear_1985 _I
> year_1986 _Iyear_1987 _Iyear_1988 _Iyear_1989 _Iyear_1990 _Iyear_1991 _Iyear_1
> 992 _Iyear_1993 _Iyear_1994 _Iyear_1995 _Iyear_1996 _Iyear_1997 _Iyear_1998 _I
> year_1999 _Iyear_2000 _Iyear_2001 _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2
> 005 _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009 _Iyear_2010 ld lnregion

2-Step GMM estimation
---------------------

Estimates efficient for arbitrary heteroskedasticity and clustering on gwf_casei
> d
Statistics robust to heteroskedasticity and clustering on gwf_caseid

Number of clusters (gwf_caseid) = 280                 Number of obs =     4559
                                                      F(  3,   279) =     9.19
                                                      Prob > F      =   0.0000
Total (centered) SS     =  72.68826002                Centered R2   =   0.0112
Total (uncentered) SS   =  72.68826002                Uncentered R2 =   0.0112
Residual SS             =   71.8731819                Root MSE      =    .1296

------------------------------------------------------------------------------
             |               Robust
        gdem | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |   -.008826   .0027792    -3.18   0.001    -.0142732   -.0033788
          ld |   .0197409   .0038213     5.17   0.000     .0122514    .0272305
    lnregion |   .0010578   .0019862     0.53   0.594    -.0028349    .0049506
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):             86.135
                                                   Chi-sq(68) P-val =   0.0680
------------------------------------------------------------------------------
Weak identification test (Kleibergen-Paap rk Wald F statistic):         16.174
Stock-Yogo weak ID test critical values:  5% maximal IV relative bias    21.21
                                         10% maximal IV relative bias    11.00
                                         20% maximal IV relative bias     5.77
                                         30% maximal IV relative bias     3.99
                                         10% maximal IV size            176.89
                                         15% maximal IV size             90.48
                                         20% maximal IV size             61.35
                                         25% maximal IV size             46.78
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):        58.333
                                                   Chi-sq(67) P-val =   0.7659
-orthog- option:
Hansen J statistic (eqn. excluding suspect orthog. conditions):         57.800
                                                   Chi-sq(65) P-val =   0.7249
C statistic (exogeneity/orthogonality of suspect instruments):           0.533
                                                   Chi-sq(2) P-val =    0.7662
Instruments tested:   militrank militrank2
------------------------------------------------------------------------------
Instrumented:         xpers2
Included instruments: ld lnregion
Excluded instruments: militrank militrank2 xpers2__Iyear_1947_g
                      xpers2__Iyear_1948_g xpers2__Iyear_1949_g
                      xpers2__Iyear_1950_g xpers2__Iyear_1951_g
                      xpers2__Iyear_1952_g xpers2__Iyear_1953_g
                      xpers2__Iyear_1954_g xpers2__Iyear_1955_g
                      xpers2__Iyear_1956_g xpers2__Iyear_1957_g
                      xpers2__Iyear_1958_g xpers2__Iyear_1959_g
                      xpers2__Iyear_1960_g xpers2__Iyear_1961_g
                      xpers2__Iyear_1962_g xpers2__Iyear_1963_g
                      xpers2__Iyear_1964_g xpers2__Iyear_1965_g
                      xpers2__Iyear_1966_g xpers2__Iyear_1967_g
                      xpers2__Iyear_1968_g xpers2__Iyear_1969_g
                      xpers2__Iyear_1970_g xpers2__Iyear_1971_g
                      xpers2__Iyear_1972_g xpers2__Iyear_1973_g
                      xpers2__Iyear_1974_g xpers2__Iyear_1975_g
                      xpers2__Iyear_1976_g xpers2__Iyear_1977_g
                      xpers2__Iyear_1978_g xpers2__Iyear_1979_g
                      xpers2__Iyear_1980_g xpers2__Iyear_1981_g
                      xpers2__Iyear_1982_g xpers2__Iyear_1983_g
                      xpers2__Iyear_1984_g xpers2__Iyear_1985_g
                      xpers2__Iyear_1986_g xpers2__Iyear_1987_g
                      xpers2__Iyear_1988_g xpers2__Iyear_1989_g
                      xpers2__Iyear_1990_g xpers2__Iyear_1991_g
                      xpers2__Iyear_1992_g xpers2__Iyear_1993_g
                      xpers2__Iyear_1994_g xpers2__Iyear_1995_g
                      xpers2__Iyear_1996_g xpers2__Iyear_1997_g
                      xpers2__Iyear_1998_g xpers2__Iyear_1999_g
                      xpers2__Iyear_2000_g xpers2__Iyear_2001_g
                      xpers2__Iyear_2002_g xpers2__Iyear_2003_g
                      xpers2__Iyear_2004_g xpers2__Iyear_2005_g
                      xpers2__Iyear_2006_g xpers2__Iyear_2007_g
                      xpers2__Iyear_2008_g xpers2__Iyear_2009_g
                      xpers2__Iyear_2010_g xpers2_ld_g xpers2_lnregion_g
Partialled-out:       _Iyear_1947 _Iyear_1948 _Iyear_1949 _Iyear_1950
                      _Iyear_1951 _Iyear_1952 _Iyear_1953 _Iyear_1954
                      _Iyear_1955 _Iyear_1956 _Iyear_1957 _Iyear_1958
                      _Iyear_1959 _Iyear_1960 _Iyear_1961 _Iyear_1962
                      _Iyear_1963 _Iyear_1964 _Iyear_1965 _Iyear_1966
                      _Iyear_1967 _Iyear_1968 _Iyear_1969 _Iyear_1970
                      _Iyear_1971 _Iyear_1972 _Iyear_1973 _Iyear_1974
                      _Iyear_1975 _Iyear_1976 _Iyear_1977 _Iyear_1978
                      _Iyear_1979 _Iyear_1980 _Iyear_1981 _Iyear_1982
                      _Iyear_1983 _Iyear_1984 _Iyear_1985 _Iyear_1986
                      _Iyear_1987 _Iyear_1988 _Iyear_1989 _Iyear_1990
                      _Iyear_1991 _Iyear_1992 _Iyear_1993 _Iyear_1994
                      _Iyear_1995 _Iyear_1996 _Iyear_1997 _Iyear_1998
                      _Iyear_1999 _Iyear_2000 _Iyear_2001 _Iyear_2002
                      _Iyear_2003 _Iyear_2004 _Iyear_2005 _Iyear_2006
                      _Iyear_2007 _Iyear_2008 _Iyear_2009 _Iyear_2010
                      nb: small-sample adjustments account for
                          partialled-out variables
------------------------------------------------------------------------------

Warning: variables have been centered

-----------------------------------------------------------
    Variable |    StdIV         GenInst       GenExtInst   
-------------+---------------------------------------------
      xpers2 |      -.08723       -.007482       -.008826  
             |        .0598         .00199         .00278  
          ld |       .05272         .01988         .01974  
             |        .0203         .00366         .00382  
    lnregion |      .004159       .0007814        .001058  
             |       .00328         .00195         .00199  
-------------+---------------------------------------------
           N |         4559           4559           4559  
        rmse |         .133            .13            .13  
           j |       .00921           59.9           58.3  
         jdf |            1             65             67  
          jp |         .924           .655           .766  
-----------------------------------------------------------
                                               Legend: b/se

.                 est store demiv3

.                 xi:ivreg2h gdem i.year l1vdem l2vdem  ld lnregion (xpers2=mili
> trank militrank2),fe cluster(gwf_caseid) partial(i.year) gmm2s
i.year            _Iyear_1946-2010    (naturally coded; _Iyear_1946 omitted)

Standard IV Results
Fixed Effects by(gwf_caseid), 234 groups
Warning - collinearities detected
Vars dropped:       _Iyear_1947 _Iyear_2010

2-Step GMM estimation
---------------------

Estimates efficient for arbitrary heteroskedasticity and clustering on gwf_casei
> d
Statistics robust to heteroskedasticity and clustering on gwf_caseid

Number of clusters (gwf_caseid) = 234                 Number of obs =     4020
                                                      F(  5,   233) =     7.93
                                                      Prob > F      =   0.0000
Total (centered) SS     =  61.23098262                Centered R2   =   0.0166
Total (uncentered) SS   =  61.23098262                Uncentered R2 =   0.0166
Residual SS             =  60.21550996                Root MSE      =    .1261

------------------------------------------------------------------------------
             |               Robust
        gdem | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0542078   .0541312    -1.00   0.317    -.1603031    .0518874
      l1vdem |   .3938283   .1600232     2.46   0.014     .0801886     .707468
      l2vdem |  -.1042899   .1462357    -0.71   0.476    -.3909066    .1823268
          ld |   .0449324   .0219693     2.05   0.041     .0018735    .0879914
    lnregion |   .0050852   .0034098     1.49   0.136    -.0015978    .0117682
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):              5.400
                                                   Chi-sq(2) P-val =    0.0672
------------------------------------------------------------------------------
Weak identification test (Kleibergen-Paap rk Wald F statistic):          3.602
Stock-Yogo weak ID test critical values: 10% maximal IV size             19.93
                                         15% maximal IV size             11.59
                                         20% maximal IV size              8.75
                                         25% maximal IV size              7.25
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.067
                                                   Chi-sq(1) P-val =    0.7951
------------------------------------------------------------------------------
Instrumented:         xpers2
Included instruments: l1vdem l2vdem ld lnregion
Excluded instruments: militrank militrank2
Partialled-out:       _Iyear_1948 _Iyear_1949 _Iyear_1950 _Iyear_1951
                      _Iyear_1952 _Iyear_1953 _Iyear_1954 _Iyear_1955
                      _Iyear_1956 _Iyear_1957 _Iyear_1958 _Iyear_1959
                      _Iyear_1960 _Iyear_1961 _Iyear_1962 _Iyear_1963
                      _Iyear_1964 _Iyear_1965 _Iyear_1966 _Iyear_1967
                      _Iyear_1968 _Iyear_1969 _Iyear_1970 _Iyear_1971
                      _Iyear_1972 _Iyear_1973 _Iyear_1974 _Iyear_1975
                      _Iyear_1976 _Iyear_1977 _Iyear_1978 _Iyear_1979
                      _Iyear_1980 _Iyear_1981 _Iyear_1982 _Iyear_1983
                      _Iyear_1984 _Iyear_1985 _Iyear_1986 _Iyear_1987
                      _Iyear_1988 _Iyear_1989 _Iyear_1990 _Iyear_1991
                      _Iyear_1992 _Iyear_1993 _Iyear_1994 _Iyear_1995
                      _Iyear_1996 _Iyear_1997 _Iyear_1998 _Iyear_1999
                      _Iyear_2000 _Iyear_2001 _Iyear_2002 _Iyear_2003
                      _Iyear_2004 _Iyear_2005 _Iyear_2006 _Iyear_2007
                      _Iyear_2008 _Iyear_2009
                      nb: small-sample adjustments account for
                          partialled-out variables
Dropped collinear:    _Iyear_1947 _Iyear_2010
------------------------------------------------------------------------------

Warning: variables have been centered

IV with Generated Instruments only
Fixed Effects by(gwf_caseid), 234 groups
Instruments created from Z:
_Iyear_1947 _Iyear_1948 _Iyear_1949 _Iyear_1950 _Iyear_1951 _Iyear_1952 _Iyear_1
> 953 _Iyear_1954 _Iyear_1955 _Iyear_1956 _Iyear_1957 _Iyear_1958 _Iyear_1959 _I
> year_1960 _Iyear_1961 _Iyear_1962 _Iyear_1963 _Iyear_1964 _Iyear_1965 _Iyear_1
> 966 _Iyear_1967 _Iyear_1968 _Iyear_1969 _Iyear_1970 _Iyear_1971 _Iyear_1972 _I
> year_1973 _Iyear_1974 _Iyear_1975 _Iyear_1976 _Iyear_1977 _Iyear_1978 _Iyear_1
> 979 _Iyear_1980 _Iyear_1981 _Iyear_1982 _Iyear_1983 _Iyear_1984 _Iyear_1985 _I
> year_1986 _Iyear_1987 _Iyear_1988 _Iyear_1989 _Iyear_1990 _Iyear_1991 _Iyear_1
> 992 _Iyear_1993 _Iyear_1994 _Iyear_1995 _Iyear_1996 _Iyear_1997 _Iyear_1998 _I
> year_1999 _Iyear_2000 _Iyear_2001 _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2
> 005 _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009 _Iyear_2010 l1vdem l2vdem 
> ld lnregion
Warning - collinearities detected
Vars dropped:       _Iyear_1947 _Iyear_2010 __00000A __000021

2-Step GMM estimation
---------------------

Estimates efficient for arbitrary heteroskedasticity and clustering on gwf_casei
> d
Statistics robust to heteroskedasticity and clustering on gwf_caseid

Number of clusters (gwf_caseid) = 234                 Number of obs =     4020
                                                      F(  5,   233) =    14.03
                                                      Prob > F      =   0.0000
Total (centered) SS     =  61.23098262                Centered R2   =   0.0252
Total (uncentered) SS   =  61.23098262                Uncentered R2 =   0.0252
Residual SS             =  59.68870278                Root MSE      =    .1256

------------------------------------------------------------------------------
             |               Robust
        gdem | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0113093   .0032222    -3.51   0.000    -.0176247   -.0049938
      l1vdem |   .3250646   .0650993     4.99   0.000     .1974723    .4526569
      l2vdem |  -.0824857   .0591924    -1.39   0.163    -.1985007    .0335292
          ld |   .0236788   .0052253     4.53   0.000     .0134374    .0339201
    lnregion |   .0008151   .0018309     0.45   0.656    -.0027733    .0044036
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):             73.656
                                                   Chi-sq(66) P-val =   0.2420
------------------------------------------------------------------------------
Weak identification test (Kleibergen-Paap rk Wald F statistic):         24.359
Stock-Yogo weak ID test critical values:  5% maximal IV relative bias    21.22
                                         10% maximal IV relative bias    11.01
                                         20% maximal IV relative bias     5.78
                                         30% maximal IV relative bias     4.00
                                         10% maximal IV size            172.11
                                         15% maximal IV size             88.07
                                         20% maximal IV size             59.74
                                         25% maximal IV size             45.57
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):        52.884
                                                   Chi-sq(65) P-val =   0.8594
------------------------------------------------------------------------------
Instrumented:         xpers2
Included instruments: l1vdem l2vdem ld lnregion
Excluded instruments: xpers2__Iyear_1948_g xpers2__Iyear_1949_g
                      xpers2__Iyear_1950_g xpers2__Iyear_1951_g
                      xpers2__Iyear_1952_g xpers2__Iyear_1953_g
                      xpers2__Iyear_1954_g xpers2__Iyear_1955_g
                      xpers2__Iyear_1956_g xpers2__Iyear_1957_g
                      xpers2__Iyear_1958_g xpers2__Iyear_1959_g
                      xpers2__Iyear_1960_g xpers2__Iyear_1961_g
                      xpers2__Iyear_1962_g xpers2__Iyear_1963_g
                      xpers2__Iyear_1964_g xpers2__Iyear_1965_g
                      xpers2__Iyear_1966_g xpers2__Iyear_1967_g
                      xpers2__Iyear_1968_g xpers2__Iyear_1969_g
                      xpers2__Iyear_1970_g xpers2__Iyear_1971_g
                      xpers2__Iyear_1972_g xpers2__Iyear_1973_g
                      xpers2__Iyear_1974_g xpers2__Iyear_1975_g
                      xpers2__Iyear_1976_g xpers2__Iyear_1977_g
                      xpers2__Iyear_1978_g xpers2__Iyear_1979_g
                      xpers2__Iyear_1980_g xpers2__Iyear_1981_g
                      xpers2__Iyear_1982_g xpers2__Iyear_1983_g
                      xpers2__Iyear_1984_g xpers2__Iyear_1985_g
                      xpers2__Iyear_1986_g xpers2__Iyear_1987_g
                      xpers2__Iyear_1988_g xpers2__Iyear_1989_g
                      xpers2__Iyear_1990_g xpers2__Iyear_1991_g
                      xpers2__Iyear_1992_g xpers2__Iyear_1993_g
                      xpers2__Iyear_1994_g xpers2__Iyear_1995_g
                      xpers2__Iyear_1996_g xpers2__Iyear_1997_g
                      xpers2__Iyear_1998_g xpers2__Iyear_1999_g
                      xpers2__Iyear_2000_g xpers2__Iyear_2001_g
                      xpers2__Iyear_2002_g xpers2__Iyear_2003_g
                      xpers2__Iyear_2004_g xpers2__Iyear_2005_g
                      xpers2__Iyear_2006_g xpers2__Iyear_2007_g
                      xpers2__Iyear_2008_g xpers2__Iyear_2009_g xpers2_l1vdem_g
                      xpers2_l2vdem_g xpers2_ld_g xpers2_lnregion_g
Partialled-out:       _Iyear_1948 _Iyear_1949 _Iyear_1950 _Iyear_1951
                      _Iyear_1952 _Iyear_1953 _Iyear_1954 _Iyear_1955
                      _Iyear_1956 _Iyear_1957 _Iyear_1958 _Iyear_1959
                      _Iyear_1960 _Iyear_1961 _Iyear_1962 _Iyear_1963
                      _Iyear_1964 _Iyear_1965 _Iyear_1966 _Iyear_1967
                      _Iyear_1968 _Iyear_1969 _Iyear_1970 _Iyear_1971
                      _Iyear_1972 _Iyear_1973 _Iyear_1974 _Iyear_1975
                      _Iyear_1976 _Iyear_1977 _Iyear_1978 _Iyear_1979
                      _Iyear_1980 _Iyear_1981 _Iyear_1982 _Iyear_1983
                      _Iyear_1984 _Iyear_1985 _Iyear_1986 _Iyear_1987
                      _Iyear_1988 _Iyear_1989 _Iyear_1990 _Iyear_1991
                      _Iyear_1992 _Iyear_1993 _Iyear_1994 _Iyear_1995
                      _Iyear_1996 _Iyear_1997 _Iyear_1998 _Iyear_1999
                      _Iyear_2000 _Iyear_2001 _Iyear_2002 _Iyear_2003
                      _Iyear_2004 _Iyear_2005 _Iyear_2006 _Iyear_2007
                      _Iyear_2008 _Iyear_2009
                      nb: small-sample adjustments account for
                          partialled-out variables
Dropped collinear:    _Iyear_1947 _Iyear_2010 xpers2__Iyear_1947_g
                      xpers2__Iyear_2010_g
------------------------------------------------------------------------------

Warning: variables have been centered

IV with Generated Instruments and External Instruments
Fixed Effects by(gwf_caseid), 234 groups
Testing Orthogonality of Instruments created from Z:
_Iyear_1947 _Iyear_1948 _Iyear_1949 _Iyear_1950 _Iyear_1951 _Iyear_1952 _Iyear_1
> 953 _Iyear_1954 _Iyear_1955 _Iyear_1956 _Iyear_1957 _Iyear_1958 _Iyear_1959 _I
> year_1960 _Iyear_1961 _Iyear_1962 _Iyear_1963 _Iyear_1964 _Iyear_1965 _Iyear_1
> 966 _Iyear_1967 _Iyear_1968 _Iyear_1969 _Iyear_1970 _Iyear_1971 _Iyear_1972 _I
> year_1973 _Iyear_1974 _Iyear_1975 _Iyear_1976 _Iyear_1977 _Iyear_1978 _Iyear_1
> 979 _Iyear_1980 _Iyear_1981 _Iyear_1982 _Iyear_1983 _Iyear_1984 _Iyear_1985 _I
> year_1986 _Iyear_1987 _Iyear_1988 _Iyear_1989 _Iyear_1990 _Iyear_1991 _Iyear_1
> 992 _Iyear_1993 _Iyear_1994 _Iyear_1995 _Iyear_1996 _Iyear_1997 _Iyear_1998 _I
> year_1999 _Iyear_2000 _Iyear_2001 _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2
> 005 _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009 _Iyear_2010 l1vdem l2vdem 
> ld lnregion
Warning - collinearities detected
Vars dropped:       _Iyear_1947 _Iyear_2010 __000027 __00003Y

2-Step GMM estimation
---------------------

Estimates efficient for arbitrary heteroskedasticity and clustering on gwf_casei
> d
Statistics robust to heteroskedasticity and clustering on gwf_caseid

Number of clusters (gwf_caseid) = 234                 Number of obs =     4020
                                                      F(  5,   233) =    14.49
                                                      Prob > F      =   0.0000
Total (centered) SS     =  61.23098262                Centered R2   =   0.0262
Total (uncentered) SS   =  61.23098262                Uncentered R2 =   0.0262
Residual SS             =   59.6280475                Root MSE      =    .1255

------------------------------------------------------------------------------
             |               Robust
        gdem | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0139311   .0037903    -3.68   0.000      -.02136   -.0065023
      l1vdem |   .3355832   .0669786     5.01   0.000     .2043076    .4668588
      l2vdem |  -.0828193   .0606568    -1.37   0.172    -.2017044    .0360658
          ld |   .0252305   .0055709     4.53   0.000     .0143117    .0361492
    lnregion |    .000846   .0018365     0.46   0.645    -.0027536    .0044455
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):             73.668
                                                   Chi-sq(68) P-val =   0.2981
------------------------------------------------------------------------------
Weak identification test (Kleibergen-Paap rk Wald F statistic):         24.869
Stock-Yogo weak ID test critical values:  5% maximal IV relative bias    21.21
                                         10% maximal IV relative bias    11.00
                                         20% maximal IV relative bias     5.77
                                         30% maximal IV relative bias     3.99
                                         10% maximal IV size            176.89
                                         15% maximal IV size             90.48
                                         20% maximal IV size             61.35
                                         25% maximal IV size             46.78
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):        52.532
                                                   Chi-sq(67) P-val =   0.9022
-orthog- option:
Hansen J statistic (eqn. excluding suspect orthog. conditions):         52.488
                                                   Chi-sq(65) P-val =   0.8681
C statistic (exogeneity/orthogonality of suspect instruments):           0.044
                                                   Chi-sq(2) P-val =    0.9783
Instruments tested:   militrank militrank2
------------------------------------------------------------------------------
Instrumented:         xpers2
Included instruments: l1vdem l2vdem ld lnregion
Excluded instruments: militrank militrank2 xpers2__Iyear_1948_g
                      xpers2__Iyear_1949_g xpers2__Iyear_1950_g
                      xpers2__Iyear_1951_g xpers2__Iyear_1952_g
                      xpers2__Iyear_1953_g xpers2__Iyear_1954_g
                      xpers2__Iyear_1955_g xpers2__Iyear_1956_g
                      xpers2__Iyear_1957_g xpers2__Iyear_1958_g
                      xpers2__Iyear_1959_g xpers2__Iyear_1960_g
                      xpers2__Iyear_1961_g xpers2__Iyear_1962_g
                      xpers2__Iyear_1963_g xpers2__Iyear_1964_g
                      xpers2__Iyear_1965_g xpers2__Iyear_1966_g
                      xpers2__Iyear_1967_g xpers2__Iyear_1968_g
                      xpers2__Iyear_1969_g xpers2__Iyear_1970_g
                      xpers2__Iyear_1971_g xpers2__Iyear_1972_g
                      xpers2__Iyear_1973_g xpers2__Iyear_1974_g
                      xpers2__Iyear_1975_g xpers2__Iyear_1976_g
                      xpers2__Iyear_1977_g xpers2__Iyear_1978_g
                      xpers2__Iyear_1979_g xpers2__Iyear_1980_g
                      xpers2__Iyear_1981_g xpers2__Iyear_1982_g
                      xpers2__Iyear_1983_g xpers2__Iyear_1984_g
                      xpers2__Iyear_1985_g xpers2__Iyear_1986_g
                      xpers2__Iyear_1987_g xpers2__Iyear_1988_g
                      xpers2__Iyear_1989_g xpers2__Iyear_1990_g
                      xpers2__Iyear_1991_g xpers2__Iyear_1992_g
                      xpers2__Iyear_1993_g xpers2__Iyear_1994_g
                      xpers2__Iyear_1995_g xpers2__Iyear_1996_g
                      xpers2__Iyear_1997_g xpers2__Iyear_1998_g
                      xpers2__Iyear_1999_g xpers2__Iyear_2000_g
                      xpers2__Iyear_2001_g xpers2__Iyear_2002_g
                      xpers2__Iyear_2003_g xpers2__Iyear_2004_g
                      xpers2__Iyear_2005_g xpers2__Iyear_2006_g
                      xpers2__Iyear_2007_g xpers2__Iyear_2008_g
                      xpers2__Iyear_2009_g xpers2_l1vdem_g xpers2_l2vdem_g
                      xpers2_ld_g xpers2_lnregion_g
Partialled-out:       _Iyear_1948 _Iyear_1949 _Iyear_1950 _Iyear_1951
                      _Iyear_1952 _Iyear_1953 _Iyear_1954 _Iyear_1955
                      _Iyear_1956 _Iyear_1957 _Iyear_1958 _Iyear_1959
                      _Iyear_1960 _Iyear_1961 _Iyear_1962 _Iyear_1963
                      _Iyear_1964 _Iyear_1965 _Iyear_1966 _Iyear_1967
                      _Iyear_1968 _Iyear_1969 _Iyear_1970 _Iyear_1971
                      _Iyear_1972 _Iyear_1973 _Iyear_1974 _Iyear_1975
                      _Iyear_1976 _Iyear_1977 _Iyear_1978 _Iyear_1979
                      _Iyear_1980 _Iyear_1981 _Iyear_1982 _Iyear_1983
                      _Iyear_1984 _Iyear_1985 _Iyear_1986 _Iyear_1987
                      _Iyear_1988 _Iyear_1989 _Iyear_1990 _Iyear_1991
                      _Iyear_1992 _Iyear_1993 _Iyear_1994 _Iyear_1995
                      _Iyear_1996 _Iyear_1997 _Iyear_1998 _Iyear_1999
                      _Iyear_2000 _Iyear_2001 _Iyear_2002 _Iyear_2003
                      _Iyear_2004 _Iyear_2005 _Iyear_2006 _Iyear_2007
                      _Iyear_2008 _Iyear_2009
                      nb: small-sample adjustments account for
                          partialled-out variables
Dropped collinear:    _Iyear_1947 _Iyear_2010 xpers2__Iyear_1947_g
                      xpers2__Iyear_2010_g
------------------------------------------------------------------------------

Warning: variables have been centered

-----------------------------------------------------------
    Variable |    StdIV         GenInst       GenExtInst   
-------------+---------------------------------------------
      xpers2 |      -.05421        -.01131        -.01393  
             |        .0541         .00322         .00379  
      l1vdem |        .3938          .3251          .3356  
             |          .16          .0651           .067  
      l2vdem |       -.1043        -.08249        -.08282  
             |         .146          .0592          .0607  
          ld |       .04493         .02368         .02523  
             |         .022         .00523         .00557  
    lnregion |      .005085       .0008151        .000846  
             |       .00341         .00183         .00184  
-------------+---------------------------------------------
           N |         4020           4020           4020  
        rmse |         .126           .126           .125  
           j |        .0674           52.9           52.5  
         jdf |            1             65             67  
          jp |         .795           .859           .902  
-----------------------------------------------------------
                                               Legend: b/se
Warning - collinearities detected
Vars dropped:  _Iyear_1947 _Iyear_2010 xpers2__Iyear_1947_g xpers2__Iyear_2010_g

.                 est store demiv4

.                 
.                 * Check with 3- and 4-lags *
.                 xi:ivreg2h gdem i.year l1vdem l2vdem l3vdem  ld lnregion (xper
> s2=militrank militrank2),fe cluster(gwf_caseid) partial(i.year) gmm2s
i.year            _Iyear_1946-2010    (naturally coded; _Iyear_1946 omitted)

Standard IV Results
Fixed Effects by(gwf_caseid), 218 groups
Warning - collinearities detected
Vars dropped:       _Iyear_1947 _Iyear_1948 _Iyear_2010

2-Step GMM estimation
---------------------

Estimates efficient for arbitrary heteroskedasticity and clustering on gwf_casei
> d
Statistics robust to heteroskedasticity and clustering on gwf_caseid

Number of clusters (gwf_caseid) = 218                 Number of obs =     3785
                                                      F(  6,   217) =     6.44
                                                      Prob > F      =   0.0000
Total (centered) SS     =  58.47108281                Centered R2   =   0.0215
Total (uncentered) SS   =  58.47108281                Uncentered R2 =   0.0215
Residual SS             =  57.21557422                Root MSE      =    .1267

------------------------------------------------------------------------------
             |               Robust
        gdem | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0517392   .0526312    -0.98   0.326    -.1548945    .0514161
      l1vdem |   .4499406   .1635136     2.75   0.006     .1294599    .7704213
      l2vdem |    -.29508    .209939    -1.41   0.160    -.7065529     .116393
      l3vdem |   .1872176   .1609335     1.16   0.245    -.1282063    .5026416
          ld |   .0470294   .0240483     1.96   0.051    -.0001045    .0941633
    lnregion |   .0052388   .0034509     1.52   0.129    -.0015248    .0120024
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):              5.832
                                                   Chi-sq(2) P-val =    0.0541
------------------------------------------------------------------------------
Weak identification test (Kleibergen-Paap rk Wald F statistic):          3.987
Stock-Yogo weak ID test critical values: 10% maximal IV size             19.93
                                         15% maximal IV size             11.59
                                         20% maximal IV size              8.75
                                         25% maximal IV size              7.25
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.104
                                                   Chi-sq(1) P-val =    0.7468
------------------------------------------------------------------------------
Instrumented:         xpers2
Included instruments: l1vdem l2vdem l3vdem ld lnregion
Excluded instruments: militrank militrank2
Partialled-out:       _Iyear_1949 _Iyear_1950 _Iyear_1951 _Iyear_1952
                      _Iyear_1953 _Iyear_1954 _Iyear_1955 _Iyear_1956
                      _Iyear_1957 _Iyear_1958 _Iyear_1959 _Iyear_1960
                      _Iyear_1961 _Iyear_1962 _Iyear_1963 _Iyear_1964
                      _Iyear_1965 _Iyear_1966 _Iyear_1967 _Iyear_1968
                      _Iyear_1969 _Iyear_1970 _Iyear_1971 _Iyear_1972
                      _Iyear_1973 _Iyear_1974 _Iyear_1975 _Iyear_1976
                      _Iyear_1977 _Iyear_1978 _Iyear_1979 _Iyear_1980
                      _Iyear_1981 _Iyear_1982 _Iyear_1983 _Iyear_1984
                      _Iyear_1985 _Iyear_1986 _Iyear_1987 _Iyear_1988
                      _Iyear_1989 _Iyear_1990 _Iyear_1991 _Iyear_1992
                      _Iyear_1993 _Iyear_1994 _Iyear_1995 _Iyear_1996
                      _Iyear_1997 _Iyear_1998 _Iyear_1999 _Iyear_2000
                      _Iyear_2001 _Iyear_2002 _Iyear_2003 _Iyear_2004
                      _Iyear_2005 _Iyear_2006 _Iyear_2007 _Iyear_2008
                      _Iyear_2009
                      nb: small-sample adjustments account for
                          partialled-out variables
Dropped collinear:    _Iyear_1947 _Iyear_1948 _Iyear_2010
------------------------------------------------------------------------------

Warning: variables have been centered

IV with Generated Instruments only
Fixed Effects by(gwf_caseid), 218 groups
Instruments created from Z:
_Iyear_1947 _Iyear_1948 _Iyear_1949 _Iyear_1950 _Iyear_1951 _Iyear_1952 _Iyear_1
> 953 _Iyear_1954 _Iyear_1955 _Iyear_1956 _Iyear_1957 _Iyear_1958 _Iyear_1959 _I
> year_1960 _Iyear_1961 _Iyear_1962 _Iyear_1963 _Iyear_1964 _Iyear_1965 _Iyear_1
> 966 _Iyear_1967 _Iyear_1968 _Iyear_1969 _Iyear_1970 _Iyear_1971 _Iyear_1972 _I
> year_1973 _Iyear_1974 _Iyear_1975 _Iyear_1976 _Iyear_1977 _Iyear_1978 _Iyear_1
> 979 _Iyear_1980 _Iyear_1981 _Iyear_1982 _Iyear_1983 _Iyear_1984 _Iyear_1985 _I
> year_1986 _Iyear_1987 _Iyear_1988 _Iyear_1989 _Iyear_1990 _Iyear_1991 _Iyear_1
> 992 _Iyear_1993 _Iyear_1994 _Iyear_1995 _Iyear_1996 _Iyear_1997 _Iyear_1998 _I
> year_1999 _Iyear_2000 _Iyear_2001 _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2
> 005 _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009 _Iyear_2010 l1vdem l2vdem 
> l3vdem ld lnregion
Warning - collinearities detected
Vars dropped:       _Iyear_1947 _Iyear_1948 _Iyear_2010 __00000A __00000B
                    __000021

2-Step GMM estimation
---------------------

Estimates efficient for arbitrary heteroskedasticity and clustering on gwf_casei
> d
Statistics robust to heteroskedasticity and clustering on gwf_caseid

Number of clusters (gwf_caseid) = 218                 Number of obs =     3785
                                                      F(  6,   217) =    11.22
                                                      Prob > F      =   0.0000
Total (centered) SS     =  58.47108281                Centered R2   =   0.0275
Total (uncentered) SS   =  58.47108281                Uncentered R2 =   0.0275
Residual SS             =  56.86152714                Root MSE      =    .1263

------------------------------------------------------------------------------
             |               Robust
        gdem | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0155871   .0030201    -5.16   0.000    -.0215064   -.0096678
      l1vdem |   .3883815   .0705969     5.50   0.000     .2500142    .5267488
      l2vdem |  -.2441236   .0575928    -4.24   0.000    -.3570035   -.1312438
      l3vdem |   .1156326   .0389093     2.97   0.003     .0393717    .1918934
          ld |   .0297672   .0059948     4.97   0.000     .0180176    .0415169
    lnregion |   .0018278   .0017465     1.05   0.295    -.0015953    .0052509
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):             74.508
                                                   Chi-sq(66) P-val =   0.2211
------------------------------------------------------------------------------
Weak identification test (Kleibergen-Paap rk Wald F statistic):         26.149
Stock-Yogo weak ID test critical values:  5% maximal IV relative bias    21.22
                                         10% maximal IV relative bias    11.01
                                         20% maximal IV relative bias     5.78
                                         30% maximal IV relative bias     4.00
                                         10% maximal IV size            172.11
                                         15% maximal IV size             88.07
                                         20% maximal IV size             59.74
                                         25% maximal IV size             45.57
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):        52.669
                                                   Chi-sq(65) P-val =   0.8641
------------------------------------------------------------------------------
Instrumented:         xpers2
Included instruments: l1vdem l2vdem l3vdem ld lnregion
Excluded instruments: xpers2__Iyear_1949_g xpers2__Iyear_1950_g
                      xpers2__Iyear_1951_g xpers2__Iyear_1952_g
                      xpers2__Iyear_1953_g xpers2__Iyear_1954_g
                      xpers2__Iyear_1955_g xpers2__Iyear_1956_g
                      xpers2__Iyear_1957_g xpers2__Iyear_1958_g
                      xpers2__Iyear_1959_g xpers2__Iyear_1960_g
                      xpers2__Iyear_1961_g xpers2__Iyear_1962_g
                      xpers2__Iyear_1963_g xpers2__Iyear_1964_g
                      xpers2__Iyear_1965_g xpers2__Iyear_1966_g
                      xpers2__Iyear_1967_g xpers2__Iyear_1968_g
                      xpers2__Iyear_1969_g xpers2__Iyear_1970_g
                      xpers2__Iyear_1971_g xpers2__Iyear_1972_g
                      xpers2__Iyear_1973_g xpers2__Iyear_1974_g
                      xpers2__Iyear_1975_g xpers2__Iyear_1976_g
                      xpers2__Iyear_1977_g xpers2__Iyear_1978_g
                      xpers2__Iyear_1979_g xpers2__Iyear_1980_g
                      xpers2__Iyear_1981_g xpers2__Iyear_1982_g
                      xpers2__Iyear_1983_g xpers2__Iyear_1984_g
                      xpers2__Iyear_1985_g xpers2__Iyear_1986_g
                      xpers2__Iyear_1987_g xpers2__Iyear_1988_g
                      xpers2__Iyear_1989_g xpers2__Iyear_1990_g
                      xpers2__Iyear_1991_g xpers2__Iyear_1992_g
                      xpers2__Iyear_1993_g xpers2__Iyear_1994_g
                      xpers2__Iyear_1995_g xpers2__Iyear_1996_g
                      xpers2__Iyear_1997_g xpers2__Iyear_1998_g
                      xpers2__Iyear_1999_g xpers2__Iyear_2000_g
                      xpers2__Iyear_2001_g xpers2__Iyear_2002_g
                      xpers2__Iyear_2003_g xpers2__Iyear_2004_g
                      xpers2__Iyear_2005_g xpers2__Iyear_2006_g
                      xpers2__Iyear_2007_g xpers2__Iyear_2008_g
                      xpers2__Iyear_2009_g xpers2_l1vdem_g xpers2_l2vdem_g
                      xpers2_l3vdem_g xpers2_ld_g xpers2_lnregion_g
Partialled-out:       _Iyear_1949 _Iyear_1950 _Iyear_1951 _Iyear_1952
                      _Iyear_1953 _Iyear_1954 _Iyear_1955 _Iyear_1956
                      _Iyear_1957 _Iyear_1958 _Iyear_1959 _Iyear_1960
                      _Iyear_1961 _Iyear_1962 _Iyear_1963 _Iyear_1964
                      _Iyear_1965 _Iyear_1966 _Iyear_1967 _Iyear_1968
                      _Iyear_1969 _Iyear_1970 _Iyear_1971 _Iyear_1972
                      _Iyear_1973 _Iyear_1974 _Iyear_1975 _Iyear_1976
                      _Iyear_1977 _Iyear_1978 _Iyear_1979 _Iyear_1980
                      _Iyear_1981 _Iyear_1982 _Iyear_1983 _Iyear_1984
                      _Iyear_1985 _Iyear_1986 _Iyear_1987 _Iyear_1988
                      _Iyear_1989 _Iyear_1990 _Iyear_1991 _Iyear_1992
                      _Iyear_1993 _Iyear_1994 _Iyear_1995 _Iyear_1996
                      _Iyear_1997 _Iyear_1998 _Iyear_1999 _Iyear_2000
                      _Iyear_2001 _Iyear_2002 _Iyear_2003 _Iyear_2004
                      _Iyear_2005 _Iyear_2006 _Iyear_2007 _Iyear_2008
                      _Iyear_2009
                      nb: small-sample adjustments account for
                          partialled-out variables
Dropped collinear:    _Iyear_1947 _Iyear_1948 _Iyear_2010 xpers2__Iyear_1947_g
                      xpers2__Iyear_1948_g xpers2__Iyear_2010_g
------------------------------------------------------------------------------

Warning: variables have been centered

IV with Generated Instruments and External Instruments
Fixed Effects by(gwf_caseid), 218 groups
Testing Orthogonality of Instruments created from Z:
_Iyear_1947 _Iyear_1948 _Iyear_1949 _Iyear_1950 _Iyear_1951 _Iyear_1952 _Iyear_1
> 953 _Iyear_1954 _Iyear_1955 _Iyear_1956 _Iyear_1957 _Iyear_1958 _Iyear_1959 _I
> year_1960 _Iyear_1961 _Iyear_1962 _Iyear_1963 _Iyear_1964 _Iyear_1965 _Iyear_1
> 966 _Iyear_1967 _Iyear_1968 _Iyear_1969 _Iyear_1970 _Iyear_1971 _Iyear_1972 _I
> year_1973 _Iyear_1974 _Iyear_1975 _Iyear_1976 _Iyear_1977 _Iyear_1978 _Iyear_1
> 979 _Iyear_1980 _Iyear_1981 _Iyear_1982 _Iyear_1983 _Iyear_1984 _Iyear_1985 _I
> year_1986 _Iyear_1987 _Iyear_1988 _Iyear_1989 _Iyear_1990 _Iyear_1991 _Iyear_1
> 992 _Iyear_1993 _Iyear_1994 _Iyear_1995 _Iyear_1996 _Iyear_1997 _Iyear_1998 _I
> year_1999 _Iyear_2000 _Iyear_2001 _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2
> 005 _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009 _Iyear_2010 l1vdem l2vdem 
> l3vdem ld lnregion
Warning - collinearities detected
Vars dropped:       _Iyear_1947 _Iyear_1948 _Iyear_2010 __000028 __000029
                    __00003Z

2-Step GMM estimation
---------------------

Estimates efficient for arbitrary heteroskedasticity and clustering on gwf_casei
> d
Statistics robust to heteroskedasticity and clustering on gwf_caseid

Number of clusters (gwf_caseid) = 218                 Number of obs =     3785
                                                      F(  6,   217) =    14.24
                                                      Prob > F      =   0.0000
Total (centered) SS     =  58.47108281                Centered R2   =   0.0286
Total (uncentered) SS   =  58.47108281                Uncentered R2 =   0.0286
Residual SS             =  56.80157802                Root MSE      =    .1262

------------------------------------------------------------------------------
             |               Robust
        gdem | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0202284   .0032892    -6.15   0.000    -.0266751   -.0137816
      l1vdem |   .4091492   .0683722     5.98   0.000     .2751422    .5431562
      l2vdem |  -.2537453   .0582359    -4.36   0.000    -.3678855   -.1396051
      l3vdem |   .1152997   .0416406     2.77   0.006     .0336856    .1969137
          ld |   .0321571   .0059994     5.36   0.000     .0203985    .0439156
    lnregion |    .001766   .0017345     1.02   0.309    -.0016336    .0051656
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):             75.846
                                                   Chi-sq(68) P-val =   0.2403
------------------------------------------------------------------------------
Weak identification test (Kleibergen-Paap rk Wald F statistic):         24.779
Stock-Yogo weak ID test critical values:  5% maximal IV relative bias    21.21
                                         10% maximal IV relative bias    11.00
                                         20% maximal IV relative bias     5.77
                                         30% maximal IV relative bias     3.99
                                         10% maximal IV size            176.89
                                         15% maximal IV size             90.48
                                         20% maximal IV size             61.35
                                         25% maximal IV size             46.78
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):        54.803
                                                   Chi-sq(67) P-val =   0.8570
-orthog- option:
Hansen J statistic (eqn. excluding suspect orthog. conditions):         53.423
                                                   Chi-sq(65) P-val =   0.8469
C statistic (exogeneity/orthogonality of suspect instruments):           1.380
                                                   Chi-sq(2) P-val =    0.5016
Instruments tested:   militrank militrank2
------------------------------------------------------------------------------
Instrumented:         xpers2
Included instruments: l1vdem l2vdem l3vdem ld lnregion
Excluded instruments: militrank militrank2 xpers2__Iyear_1949_g
                      xpers2__Iyear_1950_g xpers2__Iyear_1951_g
                      xpers2__Iyear_1952_g xpers2__Iyear_1953_g
                      xpers2__Iyear_1954_g xpers2__Iyear_1955_g
                      xpers2__Iyear_1956_g xpers2__Iyear_1957_g
                      xpers2__Iyear_1958_g xpers2__Iyear_1959_g
                      xpers2__Iyear_1960_g xpers2__Iyear_1961_g
                      xpers2__Iyear_1962_g xpers2__Iyear_1963_g
                      xpers2__Iyear_1964_g xpers2__Iyear_1965_g
                      xpers2__Iyear_1966_g xpers2__Iyear_1967_g
                      xpers2__Iyear_1968_g xpers2__Iyear_1969_g
                      xpers2__Iyear_1970_g xpers2__Iyear_1971_g
                      xpers2__Iyear_1972_g xpers2__Iyear_1973_g
                      xpers2__Iyear_1974_g xpers2__Iyear_1975_g
                      xpers2__Iyear_1976_g xpers2__Iyear_1977_g
                      xpers2__Iyear_1978_g xpers2__Iyear_1979_g
                      xpers2__Iyear_1980_g xpers2__Iyear_1981_g
                      xpers2__Iyear_1982_g xpers2__Iyear_1983_g
                      xpers2__Iyear_1984_g xpers2__Iyear_1985_g
                      xpers2__Iyear_1986_g xpers2__Iyear_1987_g
                      xpers2__Iyear_1988_g xpers2__Iyear_1989_g
                      xpers2__Iyear_1990_g xpers2__Iyear_1991_g
                      xpers2__Iyear_1992_g xpers2__Iyear_1993_g
                      xpers2__Iyear_1994_g xpers2__Iyear_1995_g
                      xpers2__Iyear_1996_g xpers2__Iyear_1997_g
                      xpers2__Iyear_1998_g xpers2__Iyear_1999_g
                      xpers2__Iyear_2000_g xpers2__Iyear_2001_g
                      xpers2__Iyear_2002_g xpers2__Iyear_2003_g
                      xpers2__Iyear_2004_g xpers2__Iyear_2005_g
                      xpers2__Iyear_2006_g xpers2__Iyear_2007_g
                      xpers2__Iyear_2008_g xpers2__Iyear_2009_g xpers2_l1vdem_g
                      xpers2_l2vdem_g xpers2_l3vdem_g xpers2_ld_g
                      xpers2_lnregion_g
Partialled-out:       _Iyear_1949 _Iyear_1950 _Iyear_1951 _Iyear_1952
                      _Iyear_1953 _Iyear_1954 _Iyear_1955 _Iyear_1956
                      _Iyear_1957 _Iyear_1958 _Iyear_1959 _Iyear_1960
                      _Iyear_1961 _Iyear_1962 _Iyear_1963 _Iyear_1964
                      _Iyear_1965 _Iyear_1966 _Iyear_1967 _Iyear_1968
                      _Iyear_1969 _Iyear_1970 _Iyear_1971 _Iyear_1972
                      _Iyear_1973 _Iyear_1974 _Iyear_1975 _Iyear_1976
                      _Iyear_1977 _Iyear_1978 _Iyear_1979 _Iyear_1980
                      _Iyear_1981 _Iyear_1982 _Iyear_1983 _Iyear_1984
                      _Iyear_1985 _Iyear_1986 _Iyear_1987 _Iyear_1988
                      _Iyear_1989 _Iyear_1990 _Iyear_1991 _Iyear_1992
                      _Iyear_1993 _Iyear_1994 _Iyear_1995 _Iyear_1996
                      _Iyear_1997 _Iyear_1998 _Iyear_1999 _Iyear_2000
                      _Iyear_2001 _Iyear_2002 _Iyear_2003 _Iyear_2004
                      _Iyear_2005 _Iyear_2006 _Iyear_2007 _Iyear_2008
                      _Iyear_2009
                      nb: small-sample adjustments account for
                          partialled-out variables
Dropped collinear:    _Iyear_1947 _Iyear_1948 _Iyear_2010 xpers2__Iyear_1947_g
                      xpers2__Iyear_1948_g xpers2__Iyear_2010_g
------------------------------------------------------------------------------

Warning: variables have been centered

-----------------------------------------------------------
    Variable |    StdIV         GenInst       GenExtInst   
-------------+---------------------------------------------
      xpers2 |      -.05174        -.01559        -.02023  
             |        .0526         .00302         .00329  
      l1vdem |        .4499          .3884          .4091  
             |         .164          .0706          .0684  
      l2vdem |       -.2951         -.2441         -.2537  
             |          .21          .0576          .0582  
      l3vdem |        .1872          .1156          .1153  
             |         .161          .0389          .0416  
          ld |       .04703         .02977         .03216  
             |         .024         .00599           .006  
    lnregion |      .005239        .001828        .001766  
             |       .00345         .00175         .00173  
-------------+---------------------------------------------
           N |         3785           3785           3785  
        rmse |         .127           .126           .126  
           j |         .104           52.7           54.8  
         jdf |            1             65             67  
          jp |         .747           .864           .857  
-----------------------------------------------------------
                                               Legend: b/se
Warning - collinearities detected
Vars dropped:  _Iyear_1947 _Iyear_1948 _Iyear_2010 xpers2__Iyear_1947_g
               xpers2__Iyear_1948_g xpers2__Iyear_2010_g

.                 xi:ivreg2h gdem i.year l1vdem l2vdem l3vdem l4vdem  ld lnregio
> n (xpers2=militrank militrank2),fe cluster(gwf_caseid) partial(i.year) gmm2s
i.year            _Iyear_1946-2010    (naturally coded; _Iyear_1946 omitted)

Standard IV Results
Fixed Effects by(gwf_caseid), 206 groups
Warning - collinearities detected
Vars dropped:       _Iyear_1947 _Iyear_1948 _Iyear_1949 _Iyear_2010

2-Step GMM estimation
---------------------

Estimates efficient for arbitrary heteroskedasticity and clustering on gwf_casei
> d
Statistics robust to heteroskedasticity and clustering on gwf_caseid

Number of clusters (gwf_caseid) = 206                 Number of obs =     3566
                                                      F(  7,   205) =     5.52
                                                      Prob > F      =   0.0000
Total (centered) SS     =  55.02863105                Centered R2   =   0.0225
Total (uncentered) SS   =  55.02863105                Uncentered R2 =   0.0225
Residual SS             =  53.79120335                Root MSE      =    .1265

------------------------------------------------------------------------------
             |               Robust
        gdem | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0514794   .0523423    -0.98   0.325    -.1540684    .0511096
      l1vdem |   .4737859   .1707677     2.77   0.006     .1390874    .8084844
      l2vdem |  -.3071142   .2180633    -1.41   0.159    -.7345103     .120282
      l3vdem |   .2782218   .2693375     1.03   0.302    -.2496701    .8061137
      l4vdem |  -.0984201   .1828402    -0.54   0.590    -.4567803    .2599402
          ld |   .0474461   .0261758     1.81   0.070    -.0038575    .0987497
    lnregion |   .0048107   .0036347     1.32   0.186    -.0023131    .0119345
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):              5.919
                                                   Chi-sq(2) P-val =    0.0518
------------------------------------------------------------------------------
Weak identification test (Kleibergen-Paap rk Wald F statistic):          4.021
Stock-Yogo weak ID test critical values: 10% maximal IV size             19.93
                                         15% maximal IV size             11.59
                                         20% maximal IV size              8.75
                                         25% maximal IV size              7.25
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):         0.072
                                                   Chi-sq(1) P-val =    0.7887
------------------------------------------------------------------------------
Instrumented:         xpers2
Included instruments: l1vdem l2vdem l3vdem l4vdem ld lnregion
Excluded instruments: militrank militrank2
Partialled-out:       _Iyear_1950 _Iyear_1951 _Iyear_1952 _Iyear_1953
                      _Iyear_1954 _Iyear_1955 _Iyear_1956 _Iyear_1957
                      _Iyear_1958 _Iyear_1959 _Iyear_1960 _Iyear_1961
                      _Iyear_1962 _Iyear_1963 _Iyear_1964 _Iyear_1965
                      _Iyear_1966 _Iyear_1967 _Iyear_1968 _Iyear_1969
                      _Iyear_1970 _Iyear_1971 _Iyear_1972 _Iyear_1973
                      _Iyear_1974 _Iyear_1975 _Iyear_1976 _Iyear_1977
                      _Iyear_1978 _Iyear_1979 _Iyear_1980 _Iyear_1981
                      _Iyear_1982 _Iyear_1983 _Iyear_1984 _Iyear_1985
                      _Iyear_1986 _Iyear_1987 _Iyear_1988 _Iyear_1989
                      _Iyear_1990 _Iyear_1991 _Iyear_1992 _Iyear_1993
                      _Iyear_1994 _Iyear_1995 _Iyear_1996 _Iyear_1997
                      _Iyear_1998 _Iyear_1999 _Iyear_2000 _Iyear_2001
                      _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2005
                      _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009
                      nb: small-sample adjustments account for
                          partialled-out variables
Dropped collinear:    _Iyear_1947 _Iyear_1948 _Iyear_1949 _Iyear_2010
------------------------------------------------------------------------------

Warning: variables have been centered

IV with Generated Instruments only
Fixed Effects by(gwf_caseid), 206 groups
Instruments created from Z:
_Iyear_1947 _Iyear_1948 _Iyear_1949 _Iyear_1950 _Iyear_1951 _Iyear_1952 _Iyear_1
> 953 _Iyear_1954 _Iyear_1955 _Iyear_1956 _Iyear_1957 _Iyear_1958 _Iyear_1959 _I
> year_1960 _Iyear_1961 _Iyear_1962 _Iyear_1963 _Iyear_1964 _Iyear_1965 _Iyear_1
> 966 _Iyear_1967 _Iyear_1968 _Iyear_1969 _Iyear_1970 _Iyear_1971 _Iyear_1972 _I
> year_1973 _Iyear_1974 _Iyear_1975 _Iyear_1976 _Iyear_1977 _Iyear_1978 _Iyear_1
> 979 _Iyear_1980 _Iyear_1981 _Iyear_1982 _Iyear_1983 _Iyear_1984 _Iyear_1985 _I
> year_1986 _Iyear_1987 _Iyear_1988 _Iyear_1989 _Iyear_1990 _Iyear_1991 _Iyear_1
> 992 _Iyear_1993 _Iyear_1994 _Iyear_1995 _Iyear_1996 _Iyear_1997 _Iyear_1998 _I
> year_1999 _Iyear_2000 _Iyear_2001 _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2
> 005 _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009 _Iyear_2010 l1vdem l2vdem 
> l3vdem l4vdem ld lnregion
Warning - collinearities detected
Vars dropped:       _Iyear_1947 _Iyear_1948 _Iyear_1949 _Iyear_2010 __00000A
                    __00000B __00000C __000021

2-Step GMM estimation
---------------------

Estimates efficient for arbitrary heteroskedasticity and clustering on gwf_casei
> d
Statistics robust to heteroskedasticity and clustering on gwf_caseid

Number of clusters (gwf_caseid) = 206                 Number of obs =     3566
                                                      F(  7,   205) =    15.22
                                                      Prob > F      =   0.0000
Total (centered) SS     =  55.02863105                Centered R2   =   0.0276
Total (uncentered) SS   =  55.02863105                Uncentered R2 =   0.0276
Residual SS             =  53.51033037                Root MSE      =    .1262

------------------------------------------------------------------------------
             |               Robust
        gdem | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0137976   .0022325    -6.18   0.000    -.0181732   -.0094219
      l1vdem |   .4282856   .0652759     6.56   0.000     .3003471     .556224
      l2vdem |  -.2974611   .0592992    -5.02   0.000    -.4136854   -.1812367
      l3vdem |   .2179811   .0707577     3.08   0.002     .0792986    .3566636
      l4vdem |  -.0547349   .0619575    -0.88   0.377    -.1761694    .0666996
          ld |   .0249239   .0055909     4.46   0.000     .0139659    .0358819
    lnregion |   .0010511   .0016449     0.64   0.523    -.0021729    .0042751
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):             72.521
                                                   Chi-sq(66) P-val =   0.2718
------------------------------------------------------------------------------
Weak identification test (Kleibergen-Paap rk Wald F statistic):         45.414
Stock-Yogo weak ID test critical values:  5% maximal IV relative bias    21.22
                                         10% maximal IV relative bias    11.01
                                         20% maximal IV relative bias     5.78
                                         30% maximal IV relative bias     4.00
                                         10% maximal IV size            172.11
                                         15% maximal IV size             88.07
                                         20% maximal IV size             59.74
                                         25% maximal IV size             45.57
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):        59.665
                                                   Chi-sq(65) P-val =   0.6636
------------------------------------------------------------------------------
Instrumented:         xpers2
Included instruments: l1vdem l2vdem l3vdem l4vdem ld lnregion
Excluded instruments: xpers2__Iyear_1950_g xpers2__Iyear_1951_g
                      xpers2__Iyear_1952_g xpers2__Iyear_1953_g
                      xpers2__Iyear_1954_g xpers2__Iyear_1955_g
                      xpers2__Iyear_1956_g xpers2__Iyear_1957_g
                      xpers2__Iyear_1958_g xpers2__Iyear_1959_g
                      xpers2__Iyear_1960_g xpers2__Iyear_1961_g
                      xpers2__Iyear_1962_g xpers2__Iyear_1963_g
                      xpers2__Iyear_1964_g xpers2__Iyear_1965_g
                      xpers2__Iyear_1966_g xpers2__Iyear_1967_g
                      xpers2__Iyear_1968_g xpers2__Iyear_1969_g
                      xpers2__Iyear_1970_g xpers2__Iyear_1971_g
                      xpers2__Iyear_1972_g xpers2__Iyear_1973_g
                      xpers2__Iyear_1974_g xpers2__Iyear_1975_g
                      xpers2__Iyear_1976_g xpers2__Iyear_1977_g
                      xpers2__Iyear_1978_g xpers2__Iyear_1979_g
                      xpers2__Iyear_1980_g xpers2__Iyear_1981_g
                      xpers2__Iyear_1982_g xpers2__Iyear_1983_g
                      xpers2__Iyear_1984_g xpers2__Iyear_1985_g
                      xpers2__Iyear_1986_g xpers2__Iyear_1987_g
                      xpers2__Iyear_1988_g xpers2__Iyear_1989_g
                      xpers2__Iyear_1990_g xpers2__Iyear_1991_g
                      xpers2__Iyear_1992_g xpers2__Iyear_1993_g
                      xpers2__Iyear_1994_g xpers2__Iyear_1995_g
                      xpers2__Iyear_1996_g xpers2__Iyear_1997_g
                      xpers2__Iyear_1998_g xpers2__Iyear_1999_g
                      xpers2__Iyear_2000_g xpers2__Iyear_2001_g
                      xpers2__Iyear_2002_g xpers2__Iyear_2003_g
                      xpers2__Iyear_2004_g xpers2__Iyear_2005_g
                      xpers2__Iyear_2006_g xpers2__Iyear_2007_g
                      xpers2__Iyear_2008_g xpers2__Iyear_2009_g xpers2_l1vdem_g
                      xpers2_l2vdem_g xpers2_l3vdem_g xpers2_l4vdem_g
                      xpers2_ld_g xpers2_lnregion_g
Partialled-out:       _Iyear_1950 _Iyear_1951 _Iyear_1952 _Iyear_1953
                      _Iyear_1954 _Iyear_1955 _Iyear_1956 _Iyear_1957
                      _Iyear_1958 _Iyear_1959 _Iyear_1960 _Iyear_1961
                      _Iyear_1962 _Iyear_1963 _Iyear_1964 _Iyear_1965
                      _Iyear_1966 _Iyear_1967 _Iyear_1968 _Iyear_1969
                      _Iyear_1970 _Iyear_1971 _Iyear_1972 _Iyear_1973
                      _Iyear_1974 _Iyear_1975 _Iyear_1976 _Iyear_1977
                      _Iyear_1978 _Iyear_1979 _Iyear_1980 _Iyear_1981
                      _Iyear_1982 _Iyear_1983 _Iyear_1984 _Iyear_1985
                      _Iyear_1986 _Iyear_1987 _Iyear_1988 _Iyear_1989
                      _Iyear_1990 _Iyear_1991 _Iyear_1992 _Iyear_1993
                      _Iyear_1994 _Iyear_1995 _Iyear_1996 _Iyear_1997
                      _Iyear_1998 _Iyear_1999 _Iyear_2000 _Iyear_2001
                      _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2005
                      _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009
                      nb: small-sample adjustments account for
                          partialled-out variables
Dropped collinear:    _Iyear_1947 _Iyear_1948 _Iyear_1949 _Iyear_2010
                      xpers2__Iyear_1947_g xpers2__Iyear_1948_g
                      xpers2__Iyear_1949_g xpers2__Iyear_2010_g
------------------------------------------------------------------------------

Warning: variables have been centered

IV with Generated Instruments and External Instruments
Fixed Effects by(gwf_caseid), 206 groups
Testing Orthogonality of Instruments created from Z:
_Iyear_1947 _Iyear_1948 _Iyear_1949 _Iyear_1950 _Iyear_1951 _Iyear_1952 _Iyear_1
> 953 _Iyear_1954 _Iyear_1955 _Iyear_1956 _Iyear_1957 _Iyear_1958 _Iyear_1959 _I
> year_1960 _Iyear_1961 _Iyear_1962 _Iyear_1963 _Iyear_1964 _Iyear_1965 _Iyear_1
> 966 _Iyear_1967 _Iyear_1968 _Iyear_1969 _Iyear_1970 _Iyear_1971 _Iyear_1972 _I
> year_1973 _Iyear_1974 _Iyear_1975 _Iyear_1976 _Iyear_1977 _Iyear_1978 _Iyear_1
> 979 _Iyear_1980 _Iyear_1981 _Iyear_1982 _Iyear_1983 _Iyear_1984 _Iyear_1985 _I
> year_1986 _Iyear_1987 _Iyear_1988 _Iyear_1989 _Iyear_1990 _Iyear_1991 _Iyear_1
> 992 _Iyear_1993 _Iyear_1994 _Iyear_1995 _Iyear_1996 _Iyear_1997 _Iyear_1998 _I
> year_1999 _Iyear_2000 _Iyear_2001 _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2
> 005 _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009 _Iyear_2010 l1vdem l2vdem 
> l3vdem l4vdem ld lnregion
Warning - collinearities detected
Vars dropped:       _Iyear_1947 _Iyear_1948 _Iyear_1949 _Iyear_2010 __000029
                    __00002A __00002B __000040

2-Step GMM estimation
---------------------

Estimates efficient for arbitrary heteroskedasticity and clustering on gwf_casei
> d
Statistics robust to heteroskedasticity and clustering on gwf_caseid

Number of clusters (gwf_caseid) = 206                 Number of obs =     3566
                                                      F(  7,   205) =    16.61
                                                      Prob > F      =   0.0000
Total (centered) SS     =  55.02863105                Centered R2   =   0.0285
Total (uncentered) SS   =  55.02863105                Uncentered R2 =   0.0285
Residual SS             =  53.45854256                Root MSE      =    .1261

------------------------------------------------------------------------------
             |               Robust
        gdem | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      xpers2 |  -.0176149   .0027934    -6.31   0.000    -.0230899     -.01214
      l1vdem |   .4239693   .0642314     6.60   0.000     .2980781    .5498604
      l2vdem |  -.2849655   .0594387    -4.79   0.000    -.4014633   -.1684677
      l3vdem |   .2188347   .0672605     3.25   0.001     .0870066    .3506628
      l4vdem |  -.0533141   .0627564    -0.85   0.396    -.1763143    .0696861
          ld |   .0261298   .0056858     4.60   0.000     .0149859    .0372737
    lnregion |   .0010396   .0016782     0.62   0.536    -.0022497    .0043289
------------------------------------------------------------------------------
Underidentification test (Kleibergen-Paap rk LM statistic):             73.064
                                                   Chi-sq(68) P-val =   0.3153
------------------------------------------------------------------------------
Weak identification test (Kleibergen-Paap rk Wald F statistic):         51.823
Stock-Yogo weak ID test critical values:  5% maximal IV relative bias    21.21
                                         10% maximal IV relative bias    11.00
                                         20% maximal IV relative bias     5.77
                                         30% maximal IV relative bias     3.99
                                         10% maximal IV size            176.89
                                         15% maximal IV size             90.48
                                         20% maximal IV size             61.35
                                         25% maximal IV size             46.78
Source: Stock-Yogo (2005).  Reproduced by permission.
NB: Critical values are for Cragg-Donald F statistic and i.i.d. errors.
------------------------------------------------------------------------------
Hansen J statistic (overidentification test of all instruments):        60.215
                                                   Chi-sq(67) P-val =   0.7085
-orthog- option:
Hansen J statistic (eqn. excluding suspect orthog. conditions):         58.686
                                                   Chi-sq(65) P-val =   0.6963
C statistic (exogeneity/orthogonality of suspect instruments):           1.529
                                                   Chi-sq(2) P-val =    0.4655
Instruments tested:   militrank militrank2
------------------------------------------------------------------------------
Instrumented:         xpers2
Included instruments: l1vdem l2vdem l3vdem l4vdem ld lnregion
Excluded instruments: militrank militrank2 xpers2__Iyear_1950_g
                      xpers2__Iyear_1951_g xpers2__Iyear_1952_g
                      xpers2__Iyear_1953_g xpers2__Iyear_1954_g
                      xpers2__Iyear_1955_g xpers2__Iyear_1956_g
                      xpers2__Iyear_1957_g xpers2__Iyear_1958_g
                      xpers2__Iyear_1959_g xpers2__Iyear_1960_g
                      xpers2__Iyear_1961_g xpers2__Iyear_1962_g
                      xpers2__Iyear_1963_g xpers2__Iyear_1964_g
                      xpers2__Iyear_1965_g xpers2__Iyear_1966_g
                      xpers2__Iyear_1967_g xpers2__Iyear_1968_g
                      xpers2__Iyear_1969_g xpers2__Iyear_1970_g
                      xpers2__Iyear_1971_g xpers2__Iyear_1972_g
                      xpers2__Iyear_1973_g xpers2__Iyear_1974_g
                      xpers2__Iyear_1975_g xpers2__Iyear_1976_g
                      xpers2__Iyear_1977_g xpers2__Iyear_1978_g
                      xpers2__Iyear_1979_g xpers2__Iyear_1980_g
                      xpers2__Iyear_1981_g xpers2__Iyear_1982_g
                      xpers2__Iyear_1983_g xpers2__Iyear_1984_g
                      xpers2__Iyear_1985_g xpers2__Iyear_1986_g
                      xpers2__Iyear_1987_g xpers2__Iyear_1988_g
                      xpers2__Iyear_1989_g xpers2__Iyear_1990_g
                      xpers2__Iyear_1991_g xpers2__Iyear_1992_g
                      xpers2__Iyear_1993_g xpers2__Iyear_1994_g
                      xpers2__Iyear_1995_g xpers2__Iyear_1996_g
                      xpers2__Iyear_1997_g xpers2__Iyear_1998_g
                      xpers2__Iyear_1999_g xpers2__Iyear_2000_g
                      xpers2__Iyear_2001_g xpers2__Iyear_2002_g
                      xpers2__Iyear_2003_g xpers2__Iyear_2004_g
                      xpers2__Iyear_2005_g xpers2__Iyear_2006_g
                      xpers2__Iyear_2007_g xpers2__Iyear_2008_g
                      xpers2__Iyear_2009_g xpers2_l1vdem_g xpers2_l2vdem_g
                      xpers2_l3vdem_g xpers2_l4vdem_g xpers2_ld_g
                      xpers2_lnregion_g
Partialled-out:       _Iyear_1950 _Iyear_1951 _Iyear_1952 _Iyear_1953
                      _Iyear_1954 _Iyear_1955 _Iyear_1956 _Iyear_1957
                      _Iyear_1958 _Iyear_1959 _Iyear_1960 _Iyear_1961
                      _Iyear_1962 _Iyear_1963 _Iyear_1964 _Iyear_1965
                      _Iyear_1966 _Iyear_1967 _Iyear_1968 _Iyear_1969
                      _Iyear_1970 _Iyear_1971 _Iyear_1972 _Iyear_1973
                      _Iyear_1974 _Iyear_1975 _Iyear_1976 _Iyear_1977
                      _Iyear_1978 _Iyear_1979 _Iyear_1980 _Iyear_1981
                      _Iyear_1982 _Iyear_1983 _Iyear_1984 _Iyear_1985
                      _Iyear_1986 _Iyear_1987 _Iyear_1988 _Iyear_1989
                      _Iyear_1990 _Iyear_1991 _Iyear_1992 _Iyear_1993
                      _Iyear_1994 _Iyear_1995 _Iyear_1996 _Iyear_1997
                      _Iyear_1998 _Iyear_1999 _Iyear_2000 _Iyear_2001
                      _Iyear_2002 _Iyear_2003 _Iyear_2004 _Iyear_2005
                      _Iyear_2006 _Iyear_2007 _Iyear_2008 _Iyear_2009
                      nb: small-sample adjustments account for
                          partialled-out variables
Dropped collinear:    _Iyear_1947 _Iyear_1948 _Iyear_1949 _Iyear_2010
                      xpers2__Iyear_1947_g xpers2__Iyear_1948_g
                      xpers2__Iyear_1949_g xpers2__Iyear_2010_g
------------------------------------------------------------------------------

Warning: variables have been centered

-----------------------------------------------------------
    Variable |    StdIV         GenInst       GenExtInst   
-------------+---------------------------------------------
      xpers2 |      -.05148         -.0138        -.01761  
             |        .0523         .00223         .00279  
      l1vdem |        .4738          .4283           .424  
             |         .171          .0653          .0642  
      l2vdem |       -.3071         -.2975          -.285  
             |         .218          .0593          .0594  
      l3vdem |        .2782           .218          .2188  
             |         .269          .0708          .0673  
      l4vdem |      -.09842        -.05473        -.05331  
             |         .183           .062          .0628  
          ld |       .04745         .02492         .02613  
             |        .0262         .00559         .00569  
    lnregion |      .004811        .001051         .00104  
             |       .00363         .00164         .00168  
-------------+---------------------------------------------
           N |         3566           3566           3566  
        rmse |         .127           .126           .126  
           j |        .0718           59.7           60.2  
         jdf |            1             65             67  
          jp |         .789           .664           .709  
-----------------------------------------------------------
                                               Legend: b/se
Warning - collinearities detected
Vars dropped:  _Iyear_1947 _Iyear_1948 _Iyear_1949 _Iyear_2010
               xpers2__Iyear_1947_g xpers2__Iyear_1948_g xpers2__Iyear_1949_g
               xpers2__Iyear_2010_g

.                 
.                 estout demiv* using TableD3.tex, cells(b(star  fmt(%9.4f)) se(
> par fmt(%9.3f))) ///
>                         stats(r2 N N_clust widstat) style(tex) replace label s
> tarlevels(* 0.05) title(\label{tabD3})
(file TableD3.tex not found)
(output written to TableD3.tex)

.                         
.                         
.                 xi:reghdfe gdem lnregion l1vdem l2vdem l3vdem l4vdem xpers2,ab
> sorb(gwf_case_duration gwf_caseid year)cluster(gwf_caseid)
(dropped 26 singleton observations)
(MWFE estimator converged in 11 iterations)

HDFE Linear regression                            Number of obs   =      3,540
Absorbing 3 HDFE groups                           F(   6,    185) =       6.93
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1090
                                                  Adj R-squared   =     0.0167
                                                  Within R-sq.    =     0.0249
Number of clusters (gwf_caseid) =        186      Root MSE        =     0.1271

                           (Std. err. adjusted for 186 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
        gdem | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    lnregion |    .004778     .00363     1.32   0.190    -.0023834    .0119395
      l1vdem |   .4496395   .1686796     2.67   0.008     .1168565    .7824225
      l2vdem |  -.3201484    .196074    -1.63   0.104    -.7069768      .06668
      l3vdem |   .2781653   .2546815     1.09   0.276    -.2242881    .7806188
      l4vdem |  -.0769556   .1816661    -0.42   0.672    -.4353593     .281448
      xpers2 |  -.0244939   .0089372    -2.74   0.007    -.0421258   -.0068621
       _cons |   .0000607   .0000124     4.88   0.000     .0000362    .0000852
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------------+
       Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------------+---------------------------------------|
 gwf_case_duration |        81           0          81     |
        gwf_caseid |       186         186           0    *|
              year |        61           1          60     |
-----------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.                 xi:reghdfe gdem lnregion lpop loggdp logoil l1vdem l2vdem l3vd
> em l4vdem l.xpers2 xpers2,absorb(gwf_case_duration gwf_caseid year)cluster(gwf
> _caseid)
(dropped 26 singleton observations)
(MWFE estimator converged in 11 iterations)

HDFE Linear regression                            Number of obs   =      3,496
Absorbing 3 HDFE groups                           F(  10,    184) =       5.28
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1206
                                                  Adj R-squared   =     0.0274
                                                  Within R-sq.    =     0.0376
Number of clusters (gwf_caseid) =        185      Root MSE        =     0.1262

                           (Std. err. adjusted for 185 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
        gdem | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    lnregion |   .0059215   .0035829     1.65   0.100    -.0011474    .0129904
       lpopl |  -.1183606   .0294576    -4.02   0.000    -.1764786   -.0602426
      loggdp |   -.023296   .0103172    -2.26   0.025    -.0436512   -.0029408
      logoil |  -.0050198   .0073555    -0.68   0.496    -.0195318    .0094921
      l1vdem |   .4227526   .1650915     2.56   0.011     .0970369    .7484683
      l2vdem |  -.2967595   .1932198    -1.54   0.126    -.6779707    .0844518
      l3vdem |    .266542   .2569294     1.04   0.301    -.2403645    .7734484
      l4vdem |  -.0482713   .1831841    -0.26   0.792    -.4096827    .3131401
             |
      xpers2 |
         L1. |   .0355944   .0165771     2.15   0.033     .0028888    .0683001
         --. |  -.0492021    .019749    -2.49   0.014    -.0881658   -.0102384
       _cons |    1.10903   .2738286     4.05   0.000     .5687829    1.649278
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------------+
       Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------------+---------------------------------------|
 gwf_case_duration |        81           0          81     |
        gwf_caseid |       185         185           0    *|
              year |        61           1          60     |
-----------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.                 xi:reghdfe gdem lnregion lpop loggdp logoil l1vdem l2vdem l3vd
> em l4vdem xpers2,absorb(gwf_case_duration gwf_caseid year)cluster(gwf_caseid)
(dropped 26 singleton observations)
(MWFE estimator converged in 11 iterations)

HDFE Linear regression                            Number of obs   =      3,496
Absorbing 3 HDFE groups                           F(   9,    184) =       5.75
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1160
                                                  Adj R-squared   =     0.0226
                                                  Within R-sq.    =     0.0325
Number of clusters (gwf_caseid) =        185      Root MSE        =     0.1265

                           (Std. err. adjusted for 185 clusters in gwf_caseid)
------------------------------------------------------------------------------
             |               Robust
        gdem | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    lnregion |   .0058196   .0036079     1.61   0.108    -.0012986    .0129377
       lpopl |  -.1124251   .0287805    -3.91   0.000    -.1692073    -.055643
      loggdp |  -.0251401   .0104902    -2.40   0.018    -.0458365   -.0044436
      logoil |  -.0032448   .0074666    -0.43   0.664    -.0179759    .0114864
      l1vdem |   .4345308   .1657194     2.62   0.009     .1075764    .7614852
      l2vdem |  -.3121526   .1940048    -1.61   0.109    -.6949125    .0706074
      l3vdem |   .2671392   .2561587     1.04   0.298    -.2382466    .7725251
      l4vdem |  -.0526148   .1829619    -0.29   0.774    -.4135878    .3083583
      xpers2 |   -.021381   .0092558    -2.31   0.022    -.0396422   -.0031198
       _cons |   1.055171    .267691     3.94   0.000     .5270327     1.58331
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------------+
       Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------------+---------------------------------------|
 gwf_case_duration |        81           0          81     |
        gwf_caseid |       185         185           0    *|
              year |        61           1          60     |
-----------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

.         
.         
.         ********* VDEM democracy score ************
.           reghdfe v2x_polyarchy ld lnregion xpers2,a(gwf_caseid year)cluster(g
> wf_leaderid)
(dropped 24 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =      4,533
Absorbing 2 HDFE groups                           F(   3,    480) =       2.20
Statistics robust to heteroskedasticity           Prob > F        =     0.0871
                                                  R-squared       =     0.8330
                                                  Adj R-squared   =     0.8203
                                                  Within R-sq.    =     0.0099
Number of clusters (gwf_leaderid) =        481    Root MSE        =     0.0532

                         (Std. err. adjusted for 481 clusters in gwf_leaderid)
------------------------------------------------------------------------------
             |               Robust
v2x_polyar~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
          ld |   .0116213   .0060217     1.93   0.054    -.0002108    .0234535
    lnregion |    .000531   .0011606     0.46   0.648    -.0017495    .0028115
      xpers2 |  -.0090859   .0044641    -2.04   0.042    -.0178574   -.0003144
       _cons |   .2032938   .0018575   109.45   0.000      .199644    .2069435
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
  gwf_caseid |       256           0         256     |
        year |        65           1          64     |
-----------------------------------------------------+

.           egen minyr= min(year),by(gwf_caseid)

.           gen ovdem = l1v2x_poly if year==minyr
(4,282 missing values generated)

.           egen ivdem = max(ovdem),by(gwf_caseid)
(76 missing values generated)

.           reghdfe v2x_polyarchy ivdem ld lnregion xpers2,a(cowcode year)cluste
> r(gwf_leaderid)
(dropped 1 singleton observations)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =      4,481
Absorbing 2 HDFE groups                           F(   4,    497) =      20.26
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7534
                                                  Adj R-squared   =     0.7429
                                                  Within R-sq.    =     0.1129
Number of clusters (gwf_leaderid) =        498    Root MSE        =     0.0635

                         (Std. err. adjusted for 498 clusters in gwf_leaderid)
------------------------------------------------------------------------------
             |               Robust
v2x_polyar~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       ivdem |   .4894924   .0585989     8.35   0.000     .3743602    .6046245
          ld |   .0121077   .0043863     2.76   0.006     .0034898    .0207257
    lnregion |  -.0004812   .0013723    -0.35   0.726    -.0031774     .002215
      xpers2 |  -.0086269   .0045135    -1.91   0.057    -.0174948    .0002411
       _cons |   .1151503    .010998    10.47   0.000     .0935421    .1367586
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |       116           0         116     |
        year |        65           1          64     |
-----------------------------------------------------+

. 
. log close
      name:  <unnamed>
       log:  C:\Users\jgw12\Dropbox\Research\Pers-NAVCO\Pers-RENAVCO\CSW-BJPS-re
> production\Pers-Protest.log
  log type:  text
 closed on:   9 Feb 2022, 17:16:15
--------------------------------------------------------------------------------
